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

L1 - L2 Regularization

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

Self Organizing Map (SOM) - Neural Network

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

Principal Component Analysis - PCA

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

Naïve Bayes Classifier for text corpus

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

Bayesian Hyperparameter Optimization w/ Sklearn

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

Latent Dirichlet Allocation - LDA on text dataset

Week 9 - T-SNE

DeepQLearning - Gym

Week 10 - Sampling -MCMC, Gibbs, Slice

Quantum Computing w/ QISKIT