CS535: Pattern Recognition



Instructor
Sungjin Ahn
Contact: sungjin.ahn@cs.rutgers.edu (put "CS535" in the subject)

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. 

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)

Teaching Assistant
Chang Chen (cc1547@scarletmail.rutgers.edu), CBIM

Pre-Requisites

           16:198:530 or 16:198:520

Special Permission Number 
For those who needs SPN, please directly request it. [SPN Request no more SPN is available.

Expected Work
There will be a few quizzes (multiple choice). There will be a midterm exam but no final exam. There will be implementation homeworks and a final team project.

Grading
(The following percentage can be adjusted)
  • Quizzes (20%)
  • Homework (20%)
  • Midterm Exam (30%)
  • Final Projects (30%)

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.

Textbooks

  • Pattern Recognition and Machine Learning (PRML), Christopher C. Bishop, Springer, 2006 [pdf]
  • Machine Learning: A Probabilistic Perspective (MLPP), Kevin P. Murphy, MIT Press, 2012
  • Deep Learning (DL), Goodfellow, Ian and Bengio, Yoshua and Courville, Aaron, MIT Press, 2016

Canvas

        Link to Canvas

Schedule and Readings



9/3    
    - Course Introduction and Basic Concepts (reading: PRML 1 & MLPP 1)
9/5    
    - Basic Concepts (reading: PRML 1 & MLPP 1)
    - Reading: PRML 1.1-1.3
9/10  
    - Probability Distributions for ML 1
    - Reading: PRML 2.1-2.3
9/12   
    - Probability Distributions for ML 2
    - Reading: PRML 2.1-2.3
9/17
    - Fundamentals of Probabilistic Learning 1
    - Reading: PRML 2.1-2.3
9/19    
    - Fundamentals of Probabilistic Learning 2
    - Reading: PRML 2.1-2.3