The_Math_of_Intelligence

This is the Syllabus for Siraj Raval's new course "The Math of Intelligence"

Each week has a short video (released on Friday) and an associated longer video (released on Wednesday). So each weeks short video is in bold and the longer video is underneath.

Week 1 - First order optimization - derivative, partial derivative, convexity

SVM Classification with gradient descent

Week 2 - Second order optimization - Jacobian, hessian, laplacian

Newtons method for logistic regression

Week 3 - Vectors - Vector spaces, vector norms, matrices

K Means Clustering Algorithm

Week 4 - Matrix operations - Dot product, matrix inverse, projections

Convolutional Neural Network

Week 5 - Dimensionality Reduction - matrix decomposition, eigenvectors, eigenvalues

Principal Component Analysis

Week 6 - Probability terms - Random variables,Expectations,Variance

Random Forests

Week 7 - Parameter estimation - expectation maximization, bayes vs frequentist, maximum likelihood estimation

XGBoost

Week 8 - Types of Probability - joint, conditional, bayes rule, chain rule

The Fundamental Theorem of Linear Algebra)

Week 9 - T-SNE

Naive Bayes Classification

Week 10 - Sampling -MCMC, Gibbs, Slice

LDA

Week 11 - Popular Distributions - Bernoulli, uniform, multinomial

Gaussian Mixture Models

Week 12 - Reinforcement - Markov chains, Monte Carlo, Markov Decision Processes

Game Bot