| Philippos Mordohai Assistant Professor Department of Computer Science Stevens Institute of Technology Office: Lieb 215
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CS 559: Machine Learning: Fundamentals and Applications Spring 2009 |
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Location Babbio Center 310. Time Thursdays 6:15-8:45 PM. Office Hours Tuesday 4:30-6 and by e-mail. Pre-requisites Basic knowledge of probability and statistics. Having completed a graduate level course is not necessary, but some familiarity is. Basic programming in Matlab, C/C++ or Java. The project will require at a minimum about 50 lines of Matlab code Syllabus Textbook The required textbook is the following. I refer to it as DHS in the class outline. I will also use notes outside the textbook, mostly in the second half of the semester. Pattern Classification (2nd Edition) (Hardcover) by Richard O. Duda, Peter E. Hart and David G. Stork Publisher: Wiley-Interscience; 2 edition (October 2000) ISBN-10: 0471056693 ISBN-13: 978-0471056690 Errata, slides (some of which I will use during the class) and other information can be found at the book website Slides for each lecture will be posted here before the lecture. Evaluation Project (35%) Each student will select a project related to machine learning, which has to be approved by me regarding relevance and feasibility. I will provide pointers and suggestions for potential projects. Students actively involved in machine learning research can select a project related to their research, but new work has to be done during the semester. Large projects can be performed by groups of two students. Each student will briefly present his or her project in 3-5 minutes during Week 9. Final project reports and presentations are due in Week 14. 4 homework sets (20%) Homework sets will be tentatively assigned in Weeks 3, 5, 10 and 12 and will be due a week later. The penalty for late submission is 20% of the grade per day. Midterm (20%) The midterm is scheduled for Week 8 (March 19). It should only take the first hour of the class. Final (25%) The final will take place during the final exam period and will be cumulative. Outline Notes pt. 1 (pdf) (slide 65 corrected after lecture). Week 2: Bayesian decision theory (DHS Ch. 2) Notes pt. 2 (pdf) Week 3: Maximum likelihood estimation and Bayesian parameter estimation (DHS Ch.3) Notes pt. 3 (pdf) Homework 1 (pdf) Week 4: Expectation Maximization, Hidden Markov Models, Applications in Computer Vision (DHS Ch.3 + notes) Notes pt. 4 (pdf) Week 5: Principal Component Analysis (notes) Notes pt. 5 (pdf) PCA functions in Matlab (pdf). Homework 2 (pdf) Week 6: Eigenfaces, Fisher Linear Discriminant and Nonparametric techniques (DHS Ch. 3 and 4, notes) Notes pt. 6 (pdf) Week 7: Linear Discriminant Functions and Perceptron (DHS Ch. 5). Also midterm instructions. Notes pt. 7 (pdf) (slide 54 corrected since initial posting). Week 8: Midterm and Perceptron (part II)(DHS Ch. 5 + notes) Week 9: Project proposals and MSE Procedures for Linear Discriminat Functions (DHS Ch. 5) Notes pt. 8 (pdf) Week 10: Support Vector Machines (DHS Ch. 5) Notes pt. 9 (pdf) Homework 3 (pdf) Week 11: Support Vector Machines (part II) and Boosting (notes) Notes pt. 10 (pdf) Week 12: Graphical Models and Markov Random Fields (notes) Notes pt. 11 (pdf) Homework 4 (pdf) Week 13: Unsupervised Learning and Clustering (Notes and DHS Ch.10). Also instructions for project posters and presentations and final exam. Notes pt. 12 (pdf) Week 14: Project presentations |