last modified on Mon, 05 Sep 2022
Introduction and Basic Concepts
Supervised Learning Setup
Linear Regression
Linear Algebra
Weighted Least Squares
Logistic Regression
Newton’s Method
Perceptron
Exponential Family
Generalized Linear Models
Probability
Gaussian Discriminant Analysis
Naïve Bayes
Laplace Smoothing
Support Vector Machines
Python and Numpy
Kernels
Neural Networks
Bias - Variance
Regularization
Feature/Model Selection
Deep Learning
K-Means
GMM (non EM)
Expectation Maximization
Factor Analysis
Principal and Independent Component Analysis
Weak Supervision
Markov Decision Process