CS559 - Machine Learning Fundamentals and Applications, has been taught by Prof. Philippos Mordohai, at Stevens Insitutue of Technology. Please see the course slides (2016 fall) below.

**Slide 01**: Introduction and Probability Theory Review;**Slide 02**: Graphical Models, Belief Networks and Linear Algebra Review;**Slide 03**: Making Decisions and Parameter Estimation;**Slide 04**: Cross-validation, Overfitting, Naïve Bayes Classifier and Non-parametric Techniques;**Slide 05**: Principal Component Analysis(PCA) and Eigenfaces;**Slide 06**: Fisher Linear Discriminant, Generative vs. Discriminative Models, and Linear Discriminant Functions (LDF);**Slide 07**: Linear Regression and Linear Discriminant Functions (LDF) and Support Vector Machines (SVM);**Slide 08**: Logistic Regression;**Slide 09**: Ensemble Methods and Boosting;**Slide 10**: Hidden Markov Models;**Slide 11**: Introduction to Deep Learning;**Slide 12**: Unsupervised Learning (k-means, clustering) and Expectation Maximization (EM);