Chengjiang Long 龙成江

ECSE-6110: Pattern Recognition

Term: 2018 Spring

Instructor: Dr. Chengjiang Long

Time: Tuesday and Friday, 2:00pm – 3:20pm

Building/Room: JEC 4107, Rensselaer Polytechnic Institute (RPI)

Office Hour: Tuesday and Friday 3:20pm – 4:00pm by appointment

Office Hour Location: JEC 6045

Course Assistant: Il-Young Son

Course Website: www.chengjianglong.com/teachings.html

Course Overview

This course will give an introduction to the large and diverse field of pattern recognition. Topics include Introduction to Pattern Recognition, Probability theory and Linear algebra review, Basic graph concepts and Belief Network, Bayesian decision theory, Maximum-likelihood estimation, Bayesian methods, Nonparametric techniques, Principal Component Analysis, Fisher Discrimination, Linear models for regression, Linear models for classification and support vector machines, Bagging, Random Forests and Boosting, Introduction to Neural Networks and Multilayer Neural Network, Introduction to deep learning: Deep Feedforward Network and Convolutional Neural Network, Unsupervised Learning and clustering. On completion of the course, students should be sufficiently familiar with the formal theoretical structure, notation, and vocabulary of pattern recognition to be able to read and understand current technical literature. They will also have experience in the design and implementation of pattern recognition systems and be able to use those methods to program and solve practical problems.

Prerequisites

Basic probability and statistics, some linear algebra, basic programming skills. Working familiarity with Matlab, Python, Java or C/C++ will be expected.

Text Books

Richard O. Duda, Peter E. Hart, and David G. Stork, Pattern Classification, 2nd Edition, John Wiley, 2001.

Auxiliary Text Books

• Trevor Hastie, Robert Tibshirani and Jerome Friedman, The Elements of Statistical Learning, 2nd Edition, Springer, 2009.
• David Barber, Bayesian Reasoning and Machine Learning, Cambridge University Press, 2012.
• Ian Goodfellow and Yoshua Bengio and Aaron Courville, Deep Learning, MIT Press, 2016.

Grading

The students will be graded based on course participation (5%), four homework assignments (20%, some of them need programming), a midterm exam (20%), a final exam (20%), and a final project/presentation (35%).

Final grade: A(>=92), A-(>=90), B+(>=87), B(>=82), B-(>=80), C+(>=77), C(>=72), C-(>=70), D+(>=67), D(>=62), D-(>=60) and F(<60).

Late Submission Policy

Exponential penalty -- late for one day loses half, two day loses another half of the remaining, and so on and so forth.

Topics

• Introduction to Pattern Recognition
• Probability theory and Linear algebra review
• Basic graph concepts and Belief Network
• Bayesian decision theory
• Maximum-likelihood estimation
• Bayesian methods
• Nonparametric techniques
• Dimension reduction: Principal Component Analysis
• Fisher Discrimination
• Linear models for regression
• Linear models for classification and support vector machines
• Bagging, Random Forests and Boosting
• Introduction to Neural Networks and Multilayer Neural Network
• Introduction to deep learning: Deep Feedforward Network and Convolutional Neural Network
• Unsupervised learning and clustering

Course Schedule