Introduction to Machine Learning

Order, Minima and Maxima

Review of Matrix and Linear Algebra

Exploratory Data Analysis

Gene Expression Example

Principal Component Analysis

Singular Value Decomposition

Clustering

Convex sets and functions

Introduction to Classification

Random Vectors and the Multivariate Normal Distribution

Classification Methods

Probability Inequalities

Cross Validation

Empirical Risk Minimization

Introduction to Optimization Problems

Linear Regression

Sparse Linear Regression: LASSO

Support Vector Machines

Decision Trees

Bagging and Boosting

Conclusion

 

Some Prerequisite Material

Overview of Basic Probability

Expectations and Maximum Likelihood

Calculus