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

Recurrent Neural Network

Week 6 - Bayesian Probability - Bayesian vs Frequentist, Naive Bayes, Laplace Smoothing

Random Forests

Week 7 - Popular Distributions - Bernoulli, uniform, multinomial

Gaussian Mixture Models

Week 8 - Hyperparameter Optimization - Bayesian Methods, Probabilistic Machine Learning

XGBoost

Week 9 - Types of Probability - Joint, Conditional

The Fundamental Theorem of Linear Algebra

Week 10 - Sampling -MCMC, Gibbs, Slice

LDA

Week 11 - Reinforcement - Markov chains, Monte Carlo, Markov Decision Processes

Game Bot

Week 12 - Quantum Machine Learning

Quantum Computing