There is a lot of hidden treasure lying within university pages scattered across the internet. This list is an attempt to bring to light those awesome courses which make their high-quality material i.e. assignments, lectures, notes, readings & examinations available online for free.
- Systems
- Programming Languages / Compilers
- Algorithms
- CS Theory
- Introduction to CS
- Machine Learning
- Security
- Misc
- Lecture Videos
- Lecture Notes
- Assignments / Labs
- Readings
- CS 61C Great Ideas in Computer Architecture (Machine Structures) UC Berkeley
- CS 107 Computer Organization & Systems Stanford University
- CIS 194 Introduction to Haskell Penn Engineering
- Explore the joys of functional programming, using Haskell as a vehicle. The aim of the course will be to allow you to use Haskell to easily and conveniently write practical programs.
- Previous semester also available, with more exercises
- Assignments & Lectures
- Clojure Functional Programming with Clojure University of Helsinki
- The course is an introduction to functional programming with a dynamically typed language Clojure. We start with an introduction to Clojure; its syntax and development environment. Clojure has a good selection of data structures and we cover most of them. We also go through the basics of recursion and higher-order functions. The course material is in English.
- Github Page
- COS 326 Functional Programming Princeton University
- Covers functional programming concepts like closures, tail-call recursion & parallelism using the OCaml programming language
- Lectures
- Assignments
- CS 164 Hack your language! UC Berkeley
- Introduction to programming languages by designing and implementing domain-specific languages.
- Lecture Videos
- Code for Assignments
- CS 173 Programming Languages Brown University
- Course by Prof. Krishnamurthi (author of HtDP) and numerous other awesome books on programming languages. Uses a custom designed Pyret programming language to teach the concepts. There was an online class hosted in 2012, which includes all lecture videos for you to enjoy.
- Videos
- Assignments
- CS 240h Functional Systems in Haskell Stanford University
- Building software systems in Haskell
- Lecture Slides
- 3 Assignments: Lab1, Lab2, Lab3
- CS 421 Programming Languages and Compilers Univ of Illinois, Urbana-Champaign
- CS223 Purely Functional Data Structures In Elm University of Chicago
- This course teaches functional reactive programming and purely functional data structures based on Chris Okazaki's book and using the Elm programming language.
- Lectures
- Assignments
- CS 3110 Data Structures and Functional Programming Cornell University
- Another course that uses OCaml to teach alternative programming paradigms, especially functional and concurrent programming.
- Lecture Slides
- Assignments
- CS 4120 Introduction to Compilers Cornell University
- An introduction to the specification and implementation of modern compilers. Topics covered include lexical scanning, parsing, type checking, code generation and translation, an introduction to optimization, and compile-time and run-time support for modern programming languages. As part of the course, students build a working compiler for an object-oriented language.
- Syllabus
- Lectures
- Assignments
- CS 4400 Programming Languages Northeastern University
- This is a course on the study, design, and implementation of programming languages.
- The course works at two simultaneous levels: first, we will use a programming language that can demonstrate a wide variety of programming paradigms. Second, using this language, we will learn about the mechanics behind programming languages by implementing our own language(s). The two level approach usually means that we will often see how to use a certain feature, and continue by implementing it.
- Syllabus
- Lecture Notes/Resources
- Homework
- CS 4610 Programming Languages and Compilers University of Virginia
- Course that uses OCaml to teach functional programming and programming language design. Each assignment is a part of an interpreter and compiler for an object-oriented language similar to Java, and you are required to use a different language for each assignment (i.e., choose 4 from Python, JS, OCaml, Haskell, Ruby).
- Lecture Notes
- Assignments
- CS 5114 Network Programming Languages Cornell University
- An introduction to the specification and implementation of modern compilers. Topics covered include lexical scanning, parsing, type checking, code generation and translation, an introduction to optimization, and compile-time and run-time support for modern programming languages. As part of the course, students build a working compiler for an object-oriented language.
- Syllabus
- Lectures
- CS 5142 Scripting Languages Cornell University
- Perl, PHP, JavaScript, VisualBasic -- they are often-requested skills for employment, but most of us do not have the time to find out what they are all about. In this course, you learn how to use scripting languages for rapid prototyping, web programming, data processing, and application extension. Besides covering traditional programming languages concepts as they apply to scripting (e.g., dynamic typing and scoping), this course looks at new concepts rarely found in traditional languages (e.g., string interpolation, hashes, and polylingual code). Through a series of small projects, you use different languages to achieve programming tasks that highlight the strengths and weaknesses of scripting. As a side effect, you practice teaching yourself new languages.
- Syllabus
- Lectures
- Assignments
- CS 5470 Compilers University of Utah
- If you're a fan of Prof Matt's writing on his fantastic blog you ought to give this a shot. The course covers the design and implementation of compilers, and it explores related topics such as interpreters, virtual machines and runtime systems. Aside from the Prof's witty take on cheating the page has tons of interesting links on programming languages, parsing and compilers.
- Lecture Notes
- Projects
- CS 6118 Types and Semantics Cornell University
- Types and Semantics is about designing and understand programming languages, whether they be domain specific or general purpose. The goal of this class is to provide a variety of tools for designing custom (programming) languages for whatever task is at hand. Part of that will be a variety of insights on how languages work along with experiences from working with academics and industry on creating new languages such as Ceylon and Kotlin. The class focuses on types and semantics and the interplay between them. This means category theory and constructive type theory (e.g. Coq and richer variations) are ancillary topics of the class. The class also covers unconventional semantic domains such as classical linear type theory in order to both break students from convential thinking and to provide powerful targets capable of formalizing thinks like networking protocols, resource-sensitive computation, and concurrency constructs. The class project is to design and formalize a (programming) language for a purpose of the student's choosing, and assignments are designed to ensure students have had a chance to practice applying the techniques learned in class before culminating these skills in the class project.
- Syllabus
- Lectures
- CSE 341 Programming Languages University of Washington
- Covers non-imperative paradigms and languages such as Ruby, Racket, and ML and the fundamentals of programming languages.
- Lectures
- Assignments and Tests
- CSE P 501 Compiler Construction University of Washington
- Teaches understanding of how a modern compiler is structured and the major algorithms that are used to translate code from high-level to machine language. The best way to do this is to actually build a working compiler, so there will be a significant project to implement one that translates programs written in a core subset of Java into executable x86 assembly language. The compilers themselves will use scanner and parser generator tools and the default implementation language is Java.
- Lectures
- Assignments, Tests, and Solutions
- DMFP Discrete Mathematics and Functional Programming Wheaton College
- A course that teaches discrete maths concepts with functional programming
- Lecture Videos
- Assignments
- CSC 253 CPython internals: A ten-hour codewalk through the Python interpreter source code University of Rochester
- Nine lectures walking through the internals of CPython, the canonical Python interpreter implemented in C. They were from the Dynamic Languages and Software Development course taught in Fall 2014 at the University of Rochester.
- CS 61B Data Structures UC Berkeley
- In this course, you will study advanced programming techniques including data structures, encapsulation, abstract data types, interfaces, and algorithms for sorting and searching, and you will get a taste of “software engineering”—the design and implementation of large programs.
- Labs
- Lecture Videos on Youtube
- CS 473/573 Fundamental Algorithms Univ of Illinois, Urbana-Champaign
- Algorithms class covering recursion, randomization, amortization, graph algorithms, network flows and hardness. The lecture notes by Prof. Erikson are comprehensive enough to be a book by themselves. Highly recommended!
- Lecture Notes
- Labs and Exams
- CS 2150 Program & Data Representation University of Virginia
- This data structures course introduces C++, linked-lists, stacks, queues, trees, numerical representation, hash tables, priority queues, heaps, huffman coding, graphs, and x86 assembly.
- Lectures
- Assignments
- CS 4820 Introduction to Analysis of Algorithms Cornell University
- This course develops techniques used in the design and analysis of algorithms, with an emphasis on problems arising in computing applications. Example applications are drawn from systems and networks, artificial intelligence, computer vision, data mining, and computational biology. This course covers four major algorithm design techniques (greedy algorithms, divide and conquer, dynamic programming, and network flow), computability theory focusing on undecidability, computational complexity focusing on NP-completeness, and algorithmic techniques for intractable problems, including identification of structured special cases, approximation algorithms, and local search heuristics.
- Lectures
- Assignments
- Syllabus
- CSCI 104 Data Structures and Object Oriented Design
- CSCI 135 Software Design and Analysis I
- It is currently an intensive introduction to program development and problem solving. Its emphasis is on the process of designing, implementing, and evaluating small-scale programs. It is not supposed to be a C++ programming course, although much of the course is spent on the details of C++. C++ is an extremely large and complex programming language with many features that interact in unexpected ways. One does not need to know even half of the language to use it well.
- Lectures and Assignments
- CSCI 235 Software Design and Analysis II CUNY Hunter College
- Introduces algorithms for a few common problems such as sorting. Practically speaking, it furthers the students' programming skills with topics such as recursion, pointers, and exception handling, and provides a chance to improve software engineering skills and to give the students practical experience for more productive programming.
- Lectures and Assignments
- CSCI 335 Software Design and Analysis III
- This includes the introduction of hashes, heaps, various forms of trees, and graphs. It also revisits recursion and the sorting problem from a higher perspective than was presented in the prequels. On top of this, it is intended to introduce methods of algorithmic analysis.
- Lectures and Assignments
- CSE 373 Analysis of Algorithms Stony Brook University
- Prof Steven Skiena's no stranger to any student when it comes to algorithms. His seminal book has been touted by many to be best for getting that job in Google. In addition, he's also well-known for tutoring students in competitive programming competitions. If you're looking to brush up your knowledge on Algorithms, you can't go wrong with this course.
- Lecture Videos
- CS 97SI Introduction to Competitive Programming Stanford University
- Fantastic repository of theory and practice problems across various topics for students who are interested to participate in ACM-ICPC.
- Lectures and Assignments
- ECS 122A Algorithm Design and Analysis UC Davis
- Taught by Dan Gusfield in 2010, this course is an undergraduate introduction to algorithm design and analysis. It features traditional topics, such as Big Oh notation, as well as an importance on implementing specific algorithms. Also featured are sorting (in linear time), graph algorithms, depth-first search, string matching, dynamic programming, NP-completeness, approximation, and randomization.
- Syllabus
- Lecture Videos
- Assignments
- ECS 222A Graduate Level Algorithm Design and Analysis UC Davis
- This is the graduate level complement to the ECS 122A undergraduate algorithms course by Dan Gusfield in 2011. It assumes an undergrad course has already been taken in algorithms, and, while going over some undergraduate algorithms topics, focuses more on increasingly complex and advanced algorithms.
- Lecture Videos
- Syllabus
- Assignments
- 6.INT Hacking a Google Interview MIT
- This course taught in the MIT Independent Activities Period in 2009 goes over common solution to common interview questions for software engineer interviews at highly selective companies like Apple, Google, and Facebook. They cover time complexity, hash tables, binary search trees, and other common algorithm topics you should have already covered in a different course, but goes more in depth on things you wouldn't otherwise learn in class- like bitwise logic and problem solving tricks.
- Handouts
- Topics Covered
- 6.851 Advanced Data Structures MIT
- This is an advanced DS course, you must be done with the Advanced Algorithms course before attempting this one.
- Lectures Contains videos from sp2012 version, but there isn't much difference.
- Assignments contains the calendar as well.
- 6.854/18.415J Advanced Algorithms MIT
- Advanced course in algorithms by Dr. David Karger covering topics such as amortization, randomization, fingerprinting, word-level parallelism, bit scaling, dynamic programming, network flow, linear programming, fixed-parameter algorithms, and approximation algorithms.
- Register on NB to access the problem set and lectures.
- 15-451/651 Algorithms Carnegie Mellon University
- The required algorithms class that go in depth into all basic algorithms and the proofs behind them. This is one of the heavier algorithms curriculums on this page. Taught by Avrim Blum and Manuel Blum who has a Turing Award due to his contributions to algorithms. Course link includes a very comprehensive set of reference notes by Avrim Blum.
- CIS 500 Software Foundations University of Pennsylvania
- An introduction to formal verification of software using the Coq proof assistant. Topics include basic concepts of logic, computer-assisted theorem proving, functional programming, operational semantics, Hoare logic, and static type systems.
- Lectures and Assignments
- Textbook
- CS 103 Mathematical Foundations of Computing Stanford University
- CS103 is a first course in discrete math, computability theory, and complexity theory. In this course, we'll probe the limits of computer power, explore why some problems are harder to solve than others, and see how to reason with mathematical certainty.
- Links to all lectures notes and assignments are directly on the course page
- CS 173 Discrete Structures Univ of Illinois Urbana-Champaign
- This course is an introduction to the theoretical side of computer science. In it, you will learn how to construct proofs, read and write literate formal mathematics, get a quick introduction to key theory topics and become familiar with a range of standard mathematics concepts commonly used in computer science.
- Textbook Written by the professor. Includes Instructor's Guide.
- Assignments
- Exams
- CS 276 Foundations of Cryptography UC Berkeley
- This course discusses the complexity-theory foundations of modern cryptography, and looks at recent results in the field such as Fully Homomorphic Encryption, Indistinguishability Obfuscation, MPC and so on.
- CS 278 Complexity Theory UC Berkeley
- An graduate level course on complexity theory that introduces P vs NP, the power of randomness, average-case complexity, hardness of approximation, and so on.
- CS 374 Algorithms & Models of Computation (Fall 2014) University of Illinois Urbana-Champaign
- CS 498 section 374 (unofficially "CS 374") covers fundamental tools and techniques from theoretical computer science, including design and analysis of algorithms, formal languages and automata, computability, and complexity. Specific topics include regular and context-free languages, finite-state automata, recursive algorithms (including divide and conquer, backtracking, dynamic programming, and greedy algorithms), fundamental graph algorithms (including depth- and breadth-first search, topological sorting, minimum spanning trees, and shortest paths), undecidability, and NP-completeness. The course also has a strong focus on clear technical communication.
- Assignments/Exams
- Lecture Notes/Labs
- Lecture videos
- CS 3110 Data Structures and Functional Programming Cornell University
- CS 3110 (formerly CS 312) is the third programming course in the Computer Science curriculum, following CS 1110/1112 and CS 2110. The goal of the course is to help students become excellent programmers and software designers who can design and implement software that is elegant, efficient, and correct, and whose code can be maintained and reused.
- Syllabus
- Lectures
- Assignments
- CS 4810 Introduction to Theory of Computing Cornell University
- This undergraduate course provides a broad introduction to the mathematical foundations of computer science. We will examine basic computational models, especially Turing machines. The goal is to understand what problems can or cannot be solved in these models.
- Syllabus
- Lectures
- Assignments
- CS 6810 Theory of Computing Cornell University
- This graduate course gives a broad introduction to complexity theory, including classical results and recent developments. Complexity theory aims to understand the power of efficient computation (when computational resources like time and space are limited). Many compelling conceptual questions arise in this context. Most of these questions are (surprisingly?) difficult and far from being resolved. Nevertheless, a lot of progress has been made toward understanding them (and also why they are difficult). We will learn about these advances in this course. A theme will be combinatorial constructions with random-like properties, e.g., expander graphs and error-correcting codes. Some examples:
- Is finding a solution inherently more difficult than verifying it?
- Do more computational resources mean more computing power?
- Is it easier to find approximate solutions than exact ones?
- Are randomized algorithms more powerful than deterministic ones?
- Is it easier to solve problems in the average case than in the worst case?
- Are quantum computers more powerful than classical ones?
- Syllabus
- Lectures
- Assignments
- This graduate course gives a broad introduction to complexity theory, including classical results and recent developments. Complexity theory aims to understand the power of efficient computation (when computational resources like time and space are limited). Many compelling conceptual questions arise in this context. Most of these questions are (surprisingly?) difficult and far from being resolved. Nevertheless, a lot of progress has been made toward understanding them (and also why they are difficult). We will learn about these advances in this course. A theme will be combinatorial constructions with random-like properties, e.g., expander graphs and error-correcting codes. Some examples:
- CSCE 3193 Programming Paradigms University of Arkansas (Fayetteville)
- Programming in different paradigms with emphasis on object oriented programming, network programming and functional programming. Survey of programming languages, event driven programming, concurrency, software validation.
- Syllabus
- Notes
- Assignments
- Practice Exams
- CS 3220 Introduction to Scientific Computing Cornell University
- In this one-semester survey course, we introduce numerical methods for solving linear and nonlinear equations, interpolating data, computing integrals, and solving differential equations, and we describe how to use these tools wisely (we hope!) when solving scientific problems.
- Syllabus
- Lectures
- Assignments
- CS 4300 Information Retrieval Cornell University
- Studies the methods used to search for and discover information in large-scale systems. The emphasis is on information retrieval applied to textual materials, but there is some discussion of other formats.The course includes techniques for searching, browsing, and filtering information and the use of classification systems and thesauruses. The techniques are illustrated with examples from web searching and digital libraries.
- Syllabus
- Lectures
- Assignments
- 6.045 Great Ideas in Theoretical Computer Science MIT
- This course provides a challenging introduction to some of the central ideas of theoretical computer science. Beginning in antiquity, the course will progress through finite automata, circuits and decision trees, Turing machines and computability, efficient algorithms and reducibility, the P versus NP problem, NP-completeness, the power of randomness, cryptography and one-way functions, computational learning theory, and quantum computing. It examines the classes of problems that can and cannot be solved by various kinds of machines. It tries to explain the key differences between computational models that affect their power.
- Syllabus
- Lecture Notes
- Lecture Videos
- CS 10 The Beauty and Joy of Computing UC Berkeley
- CS10 is UCB's introductory computer science class, taught using the beginners' drag-and-drop language. Students learn about history, social implications, great principles, and future of computing. They also learn the joy of programming a computer using a friendly, graphical language, and will complete a substantial team programming project related to their interests.
- Snap*!* (based on Scratch by MIT).
- Curriculum
- CS 50 Introduction to Computer Science Harvard University
- CS50x is Harvard College's introduction to the intellectual enterprises of computer science and the art of programming for majors and non-majors alike, with or without prior programming experience. An entry-level course taught by David J. Malan.
- Lectures
- CS 61A Structure and Interpretation of Computer Programs [Python] UC Berkeley
- In CS 61A, we are interested in teaching you about programming, not about how to use one particular programming language. We consider a series of techniques for controlling program complexity, such as functional programming, data abstraction, and object-oriented programming. Mastery of a particular programming language is a very useful side effect of studying these general techniques. However, our hope is that once you have learned the essence of programming, you will find that picking up a new programming language is but a few days' work.
- Lecture Resources by Type
- Lecture Resources by Topic
- Additional Resources
- Practice Problems
- Extra Lectures
- CS 61AS Structure & Interpretation of Computer Programs [Racket] UC Berkeley
- A self-paced version of the CS61 Course but in Racket / Scheme. 61AS is a great introductory course that will ease you into all the amazing concepts that future CS courses will cover, so remember to keep an open mind, have fun, and always respect the data abstraction
- Lecture Videos
- Assignments and Notes
- CS 101 Computer Science 101 Stanford University
- CS101 teaches the essential ideas of Computer Science for a zero-prior-experience audience. Participants play and experiment with short bits of "computer code" to bring to life to the power and limitations of computers.
- Lectures videos will available for free after registration.
- CS 106A Programming Methodology Stanford University
- This course is the largest of the introductory programming courses and is one of the largest courses at Stanford. Topics focus on the introduction to the engineering of computer applications emphasizing modern software engineering principles: object-oriented design, decomposition, encapsulation, abstraction, and testing. Programming Methodology teaches the widely-used Java programming language along with good software engineering principles.
- Lecture Videos
- Assignments
- All materials in a zip file
- CS 106B Programming Abstractions Stanford University
- This course is the natural successor to Programming Methodology and covers such advanced programming topics as recursion, algorithmic analysis, and data abstraction using the C++ programming language, which is similar to both C and Java.
- Lectures
- Assignments
- All materials in a zip file
- CS 107 Programming Paradigms Stanford University
- Topics: Advanced memory management features of C and C++; the differences between imperative and object-oriented paradigms. The functional paradigm (using LISP) and concurrent programming (using C and C++)
- Lectures
- Assignments
- [CS 109] (http://otfried.org/courses/cs109/index.html) Programming Practice Using Scala KAIST
- This course introduces basic concepts of programming and computer science, such as dynamic and static typing, dynamic memory allocation, objects and methods, binary representation of numbers, using an editor and compiler from the command line, running programs with arguments from the commmand line, using libraries, and the use of basic data structures such as arrays, lists, sets, and maps. We will use Scala for this course.
- [Lectures] (http://otfried.org/courses/cs109/index.html)
- [Assignments] (http://otfried.org/courses/cs109/index.html)
- CS 1109 Fundamental Programming Concepts Cornell University
- This course provides an introduction to programming and problem solving using a high-level programming language. It is designed to increase your knowledge level to comfortably continue to courses CS111x. Our focus will be on generic programming concepts: variables, expressions, control structures, loops, arrays, functions, pseudocode and algorithms. You will learn how to analyze problems and convert your ideas into solutions interpretable by computers. We will use MATLAB; because it provides a productive environment, and it is widely used by all engineering communities.
- Syllabus
- Lectures
- Assignments
- CS 1110 Introduction to Computing Using Python Cornell University
- Programming and problem solving using Python. Emphasizes principles of software development, style, and testing. Topics include procedures and functions, iteration, recursion, arrays and vectors, strings, an operational model of procedure and function calls, algorithms, exceptions, object-oriented programming, and GUIs (graphical user interfaces). Weekly labs provide guided practice on the computer, with staff present to help. Assignments use graphics and GUIs to help develop fluency and understanding.
- Syllabus
- Lectures
- Assignments
- CS 1112 Introduction to Computing Using Matlab Cornell University
- Programming and problem solving using MATLAB. Emphasizes the systematic development of algorithms and programs. Topics include iteration, functions, arrays and vectors, strings, recursion, algorithms, object-oriented programming, and MATLAB graphics. Assignments are designed to build an appreciation for complexity, dimension, fuzzy data, inexact arithmetic, randomness, simulation, and the role of approximation. NO programming experience is necessary; some knowledge of Calculus is required.
- Syllabus
- Lectures
- Assignments
- Projects
- CS 1115 Introduction to Computational Science and Engineering Using Matlab Graphical User Interfaces Cornell University
- Programming and problem solving using MATLAB. Emphasizes the systematic development of algorithms and programs. Topics include iteration, functions, arrays and vectors, strings, recursion, algorithms, object-oriented programming, and MATLAB graphics. Assignments are designed to build an appreciation for complexity, dimension, fuzzy data, inexact arithmetic, randomness, simulation, and the role of approximation. NO programming experience is necessary; some knowledge of Calculus is required.
- Syllabus
- Lectures
- Projects
- CS 1130 Transition to OO Programming Cornell University
- Introduction to object-oriented concepts using Java. Assumes programming knowledge in a language like MATLAB, C, C++, or Fortran. Students who have learned Java but were not exposed heavily to OO programming are welcome.
- Syllabus
- Lectures
- Assignments
- CS 1133 Transition to Python Cornell University
- Introduction to the Python programming language. Covers the basic programming constructs of Python, including assignment, conditionals, iteration, functions, object-oriented design, arrays, and vectorized computation. Assumes programming knowledge in a language like Java, Matlab, C, C++, or Fortran.
- Syllabus
- Lectures
- Assignments
- CS 2110 Object-Oriented Programming and Data Structures Cornell University
- CS 2110 is an intermediate-level programming course and an introduction to computer science. Topics include program design and development, debugging and testing, object-oriented programming, proofs of correctness, complexity analysis, recursion, commonly used data structures, graph algorithms, and abstract data types. Java is the principal programming language. The course syllabus can easily be extracted by looking at the link to lectures.
- Syllabus
- Lectures
- Assignments
- CS 4302 Web Information Systems Cornell University
- This course will introduce you to technologies for building data-centric information systems on the World Wide Web, show the practical applications of such systems, and discuss their design and their social and policy context by examining cross-cutting issues such as citizen science, data journalism and open government. Course work involves lectures and readings as well as weekly homework assignments, and a semester-long project in which the students demonstrate their expertise in building data-centric Web information systems.
- Syllabus
- Lectures
- Assignments
- CSCE 2004 Programming Foundations I University of Arkansas (Fayetteville)
- Introductory course for students majoring in computer science or computer engineering. Software development process: problem specification, program design, implementation, testing and documentation. Programming topics: data representation, conditional and iterative statements, functions, arrays, strings, file I/O, and classes. Using C++ in a UNIX environment.
- Syllabus
- Notes
- Assignments
- Practice Exams
- CSCE 2014 Programming Foundations 2 University of Arkansas (Fayetteville)
- This course continues developing problem solving techniques by focusing on fundamental data structures and associated algorithms. Topics include: abstract data types, introduction to object-oriented programming, linked lists, stacks, queues, hash tables, binary trees, graphs, recursion, and searching and sorting algorithms. Using C++ in a UNIX environment.
- Syllabus
- Assignments
- Practice Exams
- 6.001 Structure and Interpretation of Computer Programs MIT
- Teaches big-picture computing concepts using the Scheme programming language. Students will implement programs in a variety of different programming paradigms (functional, object-oriented, logical). Heavy emphasis on function composition, code-as-data, control abstraction with continuations, and syntactic abstraction through macros. An excellent course if you are looking to build a mental framework on which to hang your programming knowledge.
- Lectures
- Textbook (epub, pdf)
- IDE
- CS1410-2 and CS2420-20 Computer Science I and II for Hackers University of Utah
- An intro course in the spirit of SICP designed by Professor Matthew Flatt (one of the lead designers of Racket and author of HtDP). Mostly Racket and C, and a bit of Java, with explanations on how high level functional programming concepts relate to the design of OOP programs. Do this one before SICP if SICP is a bit too much...
- Lectures and Assignments 1
- Lectures and Assignments 2
- Textbook
- Racket Language
- StatLearning Intro to Statistical Learning Stanford University
- This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines.
- The lectures cover all the material in An Introduction to Statistical Learning, with Applications in R which is a more approachable version of the Elements of Statistical Learning (or ESL) book.
- 11-785 Deep Learning Carnegie Mellon University
- The course presents the subject through a series of seminars and labs, which will explore it from its early beginnings, and work themselves to some of the state of the art. The seminars will cover the basics of deep learning and the underlying theory, as well as the breadth of application areas to which it has been applied, as well as the latest issues on learning from very large amounts of data. We will concentrate largely, although not entirely, on the connectionist architectures that are most commonly associated with it. Lectures and Reading Notes are available on the page.
- 10-601 Machine Learning Carnegie Mellon University
- This course covers the theory and practical algorithms for machine learning from a variety of perspectives. It covers topics such as Bayesian networks, decision tree learning, Support Vector Machines, statistical learning methods, unsupervised learning and reinforcement learning. The course covers theoretical concepts such as inductive bias, the PAC learning framework, Bayesian learning methods, margin-based learning, and Occam's Razor. Short programming assignments include hands-on experiments with various learning algorithms. This course is designed to give a graduate-level student a thorough grounding in the methodologies, technologies, mathematics and algorithms currently needed by people who do research in machine learning.
- Taught by one of the leading experts on Machine Learning - Tom Mitchell
- Lectures
- Project Ideas and Datasets
- EE103 Introduction to Matrix Methods Stanford University
- The course covers the basics of matrices and vectors, solving linear equations, least-squares methods, and many applications. It'll cover the mathematics, but the focus will be on using matrix methods in applications such as tomography, image processing, data fitting, time series prediction, finance, and many others. EE103 is based on a book that Stephen Boyd and Lieven Vandenberghe are currently writing. Students will use a new language called Julia to do computations with matrices and vectors.
- Lectures
- Book
- Assignments
- Code
- CS 109 Data Science Harvard University
- Learning from data in order to gain useful predictions and insights. This course introduces methods for five key facets of an investigation: data wrangling, cleaning, and sampling to get a suitable data set; data management to be able to access big data quickly and reliably; exploratory data analysis to generate hypotheses and intuition; prediction based on statistical methods such as regression and classification; and communication of results through visualization, stories, and interpretable summaries.
- Lectures
- Slides
- Labs and Assignments
- 2013 Lectures (slightly better)
- CS 188 Introduction to Artificial Intelligence UC Berkeley
- This course will introduce the basic ideas and techniques underlying the design of intelligent computer systems. A specific emphasis will be on the statistical and decision-theoretic modeling paradigm. By the end of this course, you will have built autonomous agents that efficiently make decisions in fully informed, partially observable and adversarial settings. Your agents will draw inferences in uncertain environments and optimize actions for arbitrary reward structures. Your machine learning algorithms will classify handwritten digits and photographs. The techniques you learn in this course apply to a wide variety of artificial intelligence problems and will serve as the foundation for further study in any application area you choose to pursue.
- Lectures
- Projects
- Exams
- CS 224d Deep Learning for Natural Language Processing Stanford University
- Natural language processing (NLP) is one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. Applications of NLP are everywhere because people communicate most everything in language: web search, advertisement, emails, customer service, language translation, radiology reports, etc. There are a large variety of underlying tasks and machine learning models powering NLP applications. Recently, deep learning approaches have obtained very high performance across many different NLP tasks. These models can often be trained with a single end-to-end model and do not require traditional, task-specific feature engineering. In this spring quarter course students will learn to implement, train, debug, visualize and invent their own neural network models. The course provides a deep excursion into cutting-edge research in deep learning applied to NLP.
- Syllabus
- Lectures and Assignments
- CS 231n Convolutional Neural Networks for Visual Recognition Stanford University
- Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. This course is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. During the 10-week course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision.
- Lecture Notes
- Github Page
- CS 287 Advanced Robotics UC Berkeley
- The course introduces the math and algorithms underneath state-of-the-art robotic systems. The majority of these techniques are heavily based on probabilistic reasoning and optimization---two areas with wide applicability in modern Artificial Intelligence. An intended side-effect of the course is to generally strengthen your expertise in these two areas.
- Lectures Notes
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- CS 4780 Machine Learning Cornell University
- This course will introduce you to technologies for building data-centric information systems on the World Wide Web, show the practical applications of such systems, and discuss their design and their social and policy context by examining cross-cutting issues such as citizen science, data journalism and open government. Course work involves lectures and readings as well as weekly homework assignments, and a semester-long project in which the students demonstrate their expertise in building data-centric Web information systems.
- Syllabus
- Lectures
- COMS 4771 Machine Learning Columbia University
- Course taught by Tony Jebara introduces topics in Machine Learning for both generative and discriminative estimation. Material will include least squares methods, Gaussian distributions, linear classification, linear regression, maximum likelihood, exponential family distributions, Bayesian networks, Bayesian inference, mixture models, the EM algorithm, graphical models, hidden Markov models, support vector machines, and kernel methods.
- Lectures and Assignments
- CS395T Statistical and Discrete Methods for Scientific Computing University of Texas
- Practical course in applying modern statistical techniques to real data, particularly bioinformatic data and large data sets. The emphasis is on efficient computation and concise coding, mostly in MATLAB and C++. Topics covered include probability theory and Bayesian inference; univariate distributions; Central Limit Theorem; generation of random deviates; tail (p-value) tests; multiple hypothesis correction; empirical distributions; model fitting; error estimation; contingency tables; multivariate normal distributions; phylogenetic clustering; Gaussian mixture models; EM methods; maximum likelihood estimation; Markov Chain Monte Carlo; principal component analysis; dynamic programming; hidden Markov models; performance measures for classifiers; support vector machines; Wiener filtering; wavelets; multidimensional interpolation; information theory.
- Lectures and Assignments
- CVX 101 Convex Optimization Stanford University
###Security
- 6.857 Computer and Network Security MIT
- Emphasis on applied cryptography and may include: basic notion of systems security, crypotographic hash functions, symmetric crypotography (one-time pad, stream ciphers, block ciphers), cryptanalysis, secret-sharing, authentication codes, public-key cryptography (encryption, digital signatures), public-key attacks, web browser security, biometrics, electronic cash, viruses, electronic voting, Assignments include a group final project. Topics may vary year to year. Lecture Notes References
- 6.858 Computer Systems Security MIT
- Design and implementation of secure computer systems. Lectures cover threat models, attacks that compromise security, and techniques for achieving security, based on recent research papers. Topics include operating system (OS) security, capabilities, information flow control, language security, network protocols, hardware security, and security in web applications.
- Taught by James Mickens and Nickolai Zeldovich
- Video Lectures and Labs
- Quizzes
- Readings
- Final Projects
- CIS 4930 / CIS 5930 Offensive Computer Security Florida State University
- Course taught by W. Owen Redwood and Xiuwen Liu. It covers a wide range of computer security topics, starting from Secure C Coding and Reverse Engineering to Penetration Testing, Exploitation and Web Application Hacking, both from the defensive and the offensive point of view.
- Lectures and Videos
- Assignments
- CS 5430 System Security Cornell University
- This course discusses security for computers and networked information systems. We focus on abstractions, principles, and defenses for implementing military as well as commercial-grade secure systems.
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- AM 207 Monte Carlo Methods and Stochastic Optimization Harvard University
- This course introduces important principles of Monte Carlo techniques and demonstrates the power of these techniques with simple (but very useful) applications. All of this in Python!
- Lecture Videos
- Assignments
- Lecture Notes
- CAP 5415 Computer Vision University of Central Florida
- An introductory level course covering the basic topics of computer vision, and introducing some fundamental approaches for computer vision research.
- Lectures and Videos
- Assignments
- CIS 581 Computer Vision and Computational Photography University of Pennsylvania
- An introductory course in computer vision and computational photography focusing on four topics: image features, image morphing, shape matching, and image search.
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- CS 75 Introduction to Game Development Tufts University
- The course taught by Ming Y. Chow teaches game development initially in PyGame through Python, before moving on to addressing all facets of game development. Topics addressed include game physics, sprites, animation, game development methodology, sound, testing, MMORPGs and online games, and addressing mobile development in Android, HTML5, and iOS. Most to all of the development is focused on PyGame for learning principles
- Text Lectures
- Assignments
- Labs
- CS 100 Open Source Software Construction UC Riverside
- This is a course on how to be a hacker. Your first four homework assignments walk you through the process of building your own unix shell. You'll be developing it as an open source project, and you will collaborate with each other at various points.
- Github Page
- Assignments
- CS 193p Developing Applications for iOS Stanford University
- Updated for iOS 7. Tools and APIs required to build applications for the iPhone and iPad platform using the iOS SDK. User interface designs for mobile devices and unique user interactions using multi-touch technologies. Object-oriented design using model-view-controller paradigm, memory management, Objective-C programming language. Other topics include: object-oriented database API, animation, multi-threading and performance considerations.
- Prerequisites: C language and object-oriented programming experience
- Recommended: Programming Abstractions
- Updated courses for iOS8 - Swift
- CS 223A Introduction to Robotics Stanford University
- The purpose of this course is to introduce you to basics of modeling, design, planning, and control of robot systems. In essence, the material treated in this course is a brief survey of relevant results from geometry, kinematics, statics, dynamics, and control.
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- CS 378 3D Reconstruction with Computer Vision UTexas
- In this lab-based class, we'll dive into practical applications of 3D reconstruction, combining hardware and software to build our own 3D environments from scratch. We'll use open-source frameworks like OpenCV to do the heavy lifting, with the focus on understanding and applying state-of-the art approaches to geometric computer vision
- Lectures
- CS 411 Software Architecture Design Bilkent University
- This course teaches the basic concepts, methods and techniques for designing software architectures. The topics include: rationale for software architecture design, modeling software architecture design, architectural styles/patterns, architectural requirements analysis, comparison and evaluation of architecture design methods, synthesis-based software architecture design, software product-line architectures, domain modeling, domain engineering and application engineering, software architecture implementation, evaluating software architecture designs.
- CS 3152 Introduction to Computer Game Development Cornell University
- A project-based course in which programmers and designers collaborate to make a computer game. This course investigates the theory and practice of developing computer games from a blend of technical, aesthetic, and cultural perspectives. Technical aspects of game architecture include software engineering, artificial intelligence, game physics, computer graphics, and networking. Aesthetic and cultural include art and modeling, sound and music, game balance, and player experience.
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- CS 4152 Advanced Topics in Computer Game Development Cornell University
- Project-based follow-up course to CS/INFO 3152. Students work in a multidisciplinary team to develop a game that incorporates innovative game technology. Advanced topics include 3D game development, mobile platforms, multiplayer gaming, and nontraditional input devices. There is a special emphasis on developing games that can be submitted to festivals and competitions, or that can be commercialized.
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- CS 4154 Analytics-driven Game Design Cornell University
- A project-based course in which programmers and designers collaborate to design, implement, and release a video game online through popular game portals. In this course, students will use the internet to gather data anonymously from players. Students will analyze this data in order to improve their game over multiple iterations. Technical aspects of this course include programming, database architecture, and statistical analysis.
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- CS 4620 Introduction to Computer Graphics Cornell University
- The study of creating, manipulating, and using visual images in the computer.
- Assignments
- Exams
- CS 4670 Introduction to Computer Vision Cornell University
- This course will provide an introduction to computer vision, with topics including image formation, feature detection, motion estimation, image mosaics, 3D shape reconstruction, and object and face detection and recognition. Applications of these techniques include building 3D maps, creating virtual characters, organizing photo and video databases, human computer interaction, video surveillance, automatic vehicle navigation, and mobile computer vision. This is a project-based course, in which you will implement several computer vision algorithms throughout the semester.
- Assignments
- Lectures
- CS 4700 Foundations of Artificial Intelligence Cornell University
- This course will provide an introduction to computer vision, with topics including image formation, feature detection, motion estimation, image mosaics, 3D shape reconstruction, and object and face detection and recognition. Applications of these techniques include building 3D maps, creating virtual characters, organizing photo and video databases, human computer interaction, video surveillance, automatic vehicle navigation, and mobile computer vision. This is a project-based course, in which you will implement several computer vision algorithms throughout the semester.
- Assignments
- Lectures
- CS 4786 Machine Learning for Data Science Cornell University
- An introductory course in machine learning, with a focus on data modeling and related methods and learning algorithms for data sciences. Tentative topic list:
- Dimensionality reduction, such as principal component analysis (PCA) and the singular value decomposition (SVD), canonical correlation analysis (CCA), independent component analysis (ICA), compressed sensing, random projection, the information bottleneck. (We expect to cover some, but probably not all, of these topics).
- Clustering, such as k-means, Gaussian mixture models, the expectation-maximization (EM) algorithm, link-based clustering. (We do not expect to cover hierarchical or spectral clustering.).
- Probabilistic-modeling topics such as graphical models, latent-variable models, inference (e.g., belief propagation), parameter learning.
- Regression will be covered if time permits.
- Assignments
- Lectures
- An introductory course in machine learning, with a focus on data modeling and related methods and learning algorithms for data sciences. Tentative topic list:
- CS 4812 Quantum Information Processing Cornell University
- Hardware that exploits quantum phenomena can dramatically alter the nature of computation. Though constructing a working quantum computer is a formidable technological challenge, there has been much recent experimental progress. In addition, the theory of quantum computation is of interest in itself, offering strikingly different perspectives on the nature of computation and information, as well as providing novel insights into the conceptual puzzles posed by the quantum theory. The course is intended both for physicists, unfamiliar with computational complexity theory or cryptography, and also for computer scientists and mathematicians, unfamiliar with quantum mechanics. The prerequisites are familiarity (and comfort) with finite dimensional vector spaces over the complex numbers, some standard group theory, and ability to count in binary.
- Syllabus
- Lectures
- CS 4860 Applied Logic Cornell University
- In addition to basic first-order logic, when taught by Computer Science this course involves elements of Formal Methods and Automated Reasoning. Formal Methods is concerned with proving properties of algorithms, specifying programming tasks and synthesizing programs from proofs. We will use formal methods tools such as interactive proof assistants (see www.nuprl.org). We will also spend two weeks on constructive type theory, the language used by the Coq and Nuprl proof assistants.
- Syllabus
- Lectures
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- CS 5150 Software Engineering Cornell University
- Introduction to the practical problems of specifying, designing, building, testing, and delivering reliable software systems
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- CS 5220 Applications of Parallel Computers Cornell University
- How do we solve the large-scale problems of science quickly on modern computers? How do we measure the performance of new or existing simulation codes, and what things can we do to make them run faster? How can we best take advantage of features like multicore processors, vector units, and graphics co-processors? These are the types of questions we will address in CS 5220, Applications of Parallel Computers. Topics include:
- Single-processor architecture, caches, and serial performance tuning
- Basics of parallel machine organization
- Distributed memory programming with MPI
- Shared memory programming with OpenMP
- Parallel patterns: data partitioning, synchronization, and load balancing
- Examples of parallel numerical algorithms
- Applications from science and engineering
- Lectures
- Assignments
- How do we solve the large-scale problems of science quickly on modern computers? How do we measure the performance of new or existing simulation codes, and what things can we do to make them run faster? How can we best take advantage of features like multicore processors, vector units, and graphics co-processors? These are the types of questions we will address in CS 5220, Applications of Parallel Computers. Topics include:
- CS 5540 Computational Techniques for Analyzing Clinical Data Cornell University
- CS5540 is a masters-level course that covers a wide range of clinical problems and their associated computational challenges. The practice of medicine is filled with digitally accessible information about patients, ranging from EKG readings to MRI images to electronic health records. This poses a huge opportunity for computer tools that make sense out of this data. Computation tools can be used to answer seemingly straightforward questions about a single patient's test results (“Does this patient have a normal heart rhythm?”), or to address vital questions about large populations (“Is there any clinical condition that affects the risks of Alzheimer”). In CS5540 we will look at many of the most important sources of clinical data and discuss the basic computational techniques used for their analysis, ranging in sophistication from current clinical practice to state-of-the-art research projects.
- Syllabus
- Lectures
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- CS 5724 Evolutionary Computation Cornell University
- This course will cover advanced topics in evolutionary algorithms and their application to open-ended computational design. The field of evolutionary computation tries to address large-scale optimization and planning problems through stochastic population-based methods. It draws inspiration from evolutionary processes in nature and in engineering, and also serves as abstract models for these phenomena. Evolutionary processes are generally weak methods that require little information about the problem domain and hence can be applied across a wide variety of applications. They are especially useful for open-ended problem domains for which little formal knowledge exists and the number of parameters is undefined, such as for the general engineering design process. This course will provide insight to a variety of evolutionary computation paradigms, such as genetic algorithms, genetic programming, and evolutionary strategies, as well as governing dynamics of co-evolution, arms races and mediocre stable states. New methods involving symbiosis models and pattern recognition will also be presented. The material will be intertwined with discussions of representations and results for design problems in a variety of problem domains including software, electronics, and mechanics.
- Syllabus
- Lectures
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- CS 6452 Evolutionary Computation Cornell University
- CS6452 focuses on datacenter networks and services. The emerging demand for web services and cloud computing have created need for large scale data centers. The hardware and software infrastructure for datacenters critically determines the functionality, performance, cost and failure tolerance of applications running on that datacenter. This course will examine design alternatives for both the hardware (networking) infrastructure, and the software infrastructure for datacenters.
- Syllabus
- Lectures
- CS 6630 Realistic Image Synthesis Cornell University
- This course will cover advanced topics in evolutionary algorithms and their application to open-ended computational design. The field of evolutionary computation tries to address large-scale optimization and planning problems through stochastic population-based methods. It draws inspiration from evolutionary processes in nature and in engineering, and also serves as abstract models for these phenomena. Evolutionary processes are generally weak methods that require little information about the problem domain and hence can be applied across a wide variety of applications. They are especially useful for open-ended problem domains for which little formal knowledge exists and the number of parameters is undefined, such as for the general engineering design process. This course will provide insight to a variety of evolutionary computation paradigms, such as genetic algorithms, genetic programming, and evolutionary strategies, as well as governing dynamics of co-evolution, arms races and mediocre stable states. New methods involving symbiosis models and pattern recognition will also be presented. The material will be intertwined with discussions of representations and results for design problems in a variety of problem domains including software, electronics, and mechanics.
- Syllabus
- Lectures
- Assignments
- Readings
- CS 6640 Realistic Image Synthesis Cornell University
- A course on the emerging applications of computation in photography. Likely topics include digital photography, unconventional cameras and optics, light field cameras, image processing for photography, techniques for combining multiple images, advanced image editing algorithms, and projector-camera systems.cornell.edu/courses/CS6630/2012sp/about.stm)
- Lectures
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- CS 6650 Computational Motion Cornell University
- Covers computational aspects of motion, broadly construed. Topics include the computer representation, modeling, analysis, and simulation of motion, and its relationship to various areas, including computational geometry, mesh generation, physical simulation, computer animation, robotics, biology, computer vision, acoustics, and spatio-temporal databases. Students implement several of the algorithms covered in the course and complete a final project. This offering will also explore the special role of motion processing in physically based sound rendering.
- CS 6670 Computer Vision Cornell University
- Introduction to computer vision. Topics include edge detection, image segmentation, stereopsis, motion and optical flow, image mosaics, 3D shape reconstruction, and object recognition. Students are required to implement several of the algorithms covered in the course and complete a final project.
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- CS 6700 Advanced Artificial Intelligence Cornell University
- CS 6840 Algorithmic Game Theory Cornell University
- Algorithmic Game Theory combines algorithmic thinking with game-theoretic, or, more generally, economic concepts. The course will study a range of topics at this interface
- Syllabus
- Lectures
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- Readings
- CSE 154 Web Programming University of Washington
- This course is an introduction to programming for the World Wide Web. Covers use of HTML, CSS, PHP, JavaScript, AJAX, and SQL.
- Lectures
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- EECS 588 Computer & Network Security University of Michigan
- Taught by J. Alex Halderman who has analyzed the security of Electronic Voting Machines in the US and over seas.
- This intensive research seminar covers foundational work and current topics in computer systems security.
- Readings
- ESM 296-4F GIS & Spatial Analysis UC Santa Barbara
- Taught by James Frew, Ben Best, and Lisa Wedding
- Focuses on specific computational languages (e.g., Python, R, shell) and tools (e.g., GDAL/OGR, InVEST, MGET, ModelBuilder) applied to the spatial analysis of environmental problems
- GitHub (includes lecture materials and labs)
- ICS 314 Software Engineering University of Hawaii
- Taught by Philip Johnson
- Introduction to software engineering using the "Athletic Software Engineering" pedagogy
- Readings
- Experiences
- Assessments
- IGME 582 Humanitarian Free & Open Source Software Development Rochester Institute of Technology
- This course provides students with exposure to the design, creation and production of Open Source Software projects. Students will be introduced to the historic intersections of technology and intellectual property rights and will become familiar with Open Source development processes, tools and practices.
- I485 / H400 Biologically Inspired Computation Indiana University
- Course taught by Luis Rocha about the multi-disciplinary field algorithms inspired by naturally occurring phenomenon. This course provides introduces the following areas: L-systems, Cellular Automata, Emergence, Genetic Algorithms, Swarm Intelligence and Artificial Immune Systems. It's aim is to cover the fundamentals and enable readers to build up a proficiency in applying various algorithms to real-world problems.
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- Open Sourced Elective: Database and Rails Intro to Ruby on Rails University of Texas
- An introductory course in Ruby on Rails open sourced by University of Texas' CS Adjunct Professor, Richard Schneeman.
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- Videos
- Info 290 Analyzing Big Data with Twitter UC Berkeley school of information
- In this course, UC Berkeley professors and Twitter engineers provide lectures on the most cutting-edge algorithms and software tools for data analytics as applied to Twitter's data. Topics include applied natural language processing algorithms such as sentiment analysis, large scale anomaly detection, real-time search, information diffusion and outbreak detection, trend detection in social streams, recommendation algorithms, and advanced frameworks for distributed computing.
- Lecture Videos
- Previous Years coursepage
- CS294 Cutting-edge Web Technologies Berkeley
- Want to learn what makes future web technologies tick? Join us for the class where we will dive into the internals of many of the newest web technologies, analyze and dissect them. We will conduct survey lectures to provide the background and overview of the area as well as invite guest lecturers from various leading projects to present their technologies.
- EECS E6893 & EECS E6895 Big Data Analytics & Advanced Big Data Analytics Columbia University
- Students will gain knowledge on analyzing Big Data. It serves as an introductory course for graduate students who are expecting to face Big Data storage, processing, analysis, visualization, and application issues on both workplaces and research environments.
- Taught by Dr. Ching-Yung Lin
- Course Site
- Assignments - Assignments are present in the Course Slides
- SCICOMP An Introduction to Efficient Scientific Computation Universität Bremen
- This is a graduate course in scientific computing created and taught by Oliver Serang in 2014, which covers topics in computer science and statistics with applications from biology. The course is designed top-down, starting with a problem and then deriving a variety of solutions from scratch.
- Topics include memoization, recurrence closed forms, string matching (sorting, hash tables, radix tries, and suffix tries), dynamic programming (e.g. Smith-Waterman and Needleman-Wunsch), Bayesian statistics (e.g. the envelope paradox), graphical models (HMMs, Viterbi, junction tree, belief propagation), FFT, and the probabilistic convolution tree.
- Lecture videos on Youtube and for direct download
- 14-740 Fundamentals of Computer Networks CMU
- This is an introductory course on Networking for graduate students. It follows a top-down approach to teaching Computer Networks, so it starts with the Application layer which most of the students are familiar with and as the course unravels we learn more about transport, network and link layers of the protocol stack.
- As far as prerequisites are concerned - basic computer, programming and probability theory background is required.
- The course site contains links to the lecture videos, reading material and assignments.