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