# CS535: Pattern Recognition (Fall 2019) ## Overview The principal purpose of this course is to introduce the student to the problems of pattern recognition through a comparative presentation of methodology and practical examples. The course particularly emphasize probabilistic approaches to pattern recognition and machine learning. The course is intended for computer science students with an applied mathematics orientation, and also for students in other programs (computer and electrical engineering, statistics, mathematics, psychology) who are interested in this area of research. ## Instructor & TA - Instructor - Sungjin Ahn (sungjin.ahn@cs.rutgers.edu) at CBIM-07 - Teaching Assistant - Chang Chen (cc1547@scarletmail.rutgers.edu) at CBIM ## Time and Location - When: Tuesday and Thursday at 3:20pm - 4:40pm, from Sep. 3 to Dec. 5 - Where: CCB 1203 ## Office Hours - TA office hour: 4~5PM on Friday (CBIM) - Instructor office hour: 9:30~10:30am on Friday (CBIM 9) ## Pre-Requisites 16:198:530 or 16:198:520 ## Textbooks 1. [Pattern Recognition and Machine Learning (PRML)](https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf), Christopher C. Bishop, Springer, 2006 1. Machine Learning: A Probabilistic Perspective (MLPP), Kevin P. Murphy, MIT Press, 2012 1. Deep Learning (DL), Goodfellow, Ian and Bengio, Yoshua and Courville, Aaron, MIT Press, 2016 ## Resources - [Canvas](https://rutgers.instructure.com/courses/33622) - [Lecture Slides](https://drive.google.com/drive/folders/1jo351ilxtITYu7vxTnIFoM-z4pqOtWVo?usp=sharing) ## Grading - Midterm/Quiz (25%) - Final Exam (35%) - Final Projects (40%) ## Topic Overview The following topics are expected to be covered (topics can be changed though): Basic ML concepts, Gaussian Models, Bayesian/Frequentist Learning, Linear Regression, Logistic Regression, Artificial Neural Networks, Mixture Models, Expectation-Maximization, Dimensionality Reduction, Kernel Methods, Support Vector Machines, Gaussian Processes, Recommender Systems, Decision Trees, Boosting, etc. ## Schedule 1. 09/03 - Basic Concepts 2. 09/05 - Basic Concepts 3. 09/10 - Basic Concepts & Fundamentals of Probabilistic Learning 1 4. 09/12 - Fundamentals of Probabilistic Learning 2 5. 09/17 - Fundamentals of Probabilistic Learning 3 6. 09/19 - Fundamentals of Probabilistic Learning 4 7. 10/01 - Linear Models for Regression 1 8. 10/03 - Linear Models for Regression 2 9. 10/08 - **Quiz** 10. 10/10 - Linear Models for Classification 1 11. 10/15 - Linear Models for Classification 2 12. 10/17 - Deep Learning - MLP 13. 10/22 - Deep Learning - CNN 14. 10/24 - Deep Learning - RNN - Chapter 4 in [Natural Language Understanding with Distributed Representation](https://arxiv.org/pdf/1511.07916.pdf) 16. 10/29 - Deep Learning - RNN cont. & Attention 17. 10/31 - Kernel Methods - Support Vector Machines 1 18. 11/05 - Kernel Methods - Support Vector Machines 2 & Gaussian Processes 1 - [GP Tutorial 1](https://www.robots.ox.ac.uk/~mebden/reports/GPtutorial.pdf), [GP Tutorial 2](https://mlss2011.comp.nus.edu.sg/uploads/Site/lect1gp.pdf) - Chapter 2 in [Gaussian Processes for Machine Learning](http://www.gaussianprocess.org/gpml/chapters/RW.pdf) 19. 11/07 - Kernel Methods - Gaussian Processes 20. 11/12 - Mixture Models and EM 21. 11/14 - Mixture Models and EM 22. 11/19 - (tentative) Decision Trees, Boosting, PCA 23. 11/21 - **Final Exam** 24. 11/26 - (tentative) Decision Trees, Boosting, PCA 25. ~~11/28~~ - Thanksgiving Break 26. 12/03 - **Project Presentation 1** 27. 12/05 - **Project Presentation 2** ## [Final Exam (On Nov 21)](https://hackmd.io/0GOhywPUS2i-hrkyOFnbZw?both) ## [Final Project](https://hackmd.io/@Tn97A1U0QG6gBtFPXRh4oQ/r1xhzryqS)