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);