The_Math_of_Intelligence

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

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

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

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

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

Week 6 - Probability Theory - Bayes Theorem, Naive Bayes, Laplace Smoothing

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

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

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

Week 10 - Quantum Machine Learning