Machine Learning Fall 2017 Notes

This high-level outline includes the major takeaway points from the topics we covered so far this semester.

Types of Machine Learning

General Themes

Model Selection

Naive Bayes

Decision Trees

Logistic Regression

Perceptron

Neural Networks

Optimization

Duality

Support Vector Machines

Kernels

Learning Theory


Principal Components Analysis

EM for Gaussian Mixture Models

Variational EM and K-Means

Bayesian Networks

Hidden Markov Models

Markov Random Fields

Structured Prediction

Reinforcement Learning

Deep Learning