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 - Probability Theory - Bayes Theorem, Naive Bayes, Laplace Smoothing

Random Forests

Week 7 - Hyperparameter Optimization - Bayesian vs Frequentist, Distributions, Bayesian Optimization

Gaussian Mixture Models

Week 8 - Stochastic Models - Generative Networks, Latent Dirichlet Allocation, Topic Modeling

LSTM Networks

Week 9 - Reinforcement - Markov chains, Monte Carlo, Markov Decision Processes

Game Bot

Week 10 - Quantum Machine Learning