Syllabus Information |
Spring 2018
Jun 03, 2026 |
|
 |
||
| COURSE SYLLABUS ECSE – 6610 Pattern Recognition - 53100 - ECSE 6610 - 01 |
||||
|---|---|---|---|---|
|
Associated Term: Spring 2018
Levels: Graduate, Undergraduate Troy Campus Lecture Schedule Type Learning Objectives: 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. Course Topics: Introduction to Pattern Recognition. 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. Basic graph concepts and Belief Network. Introduction to Neural Networks and Multilayer Neural Network Introduction to deep learning: Deep Feedforward Network and Convolutional Neural Network. Unsupervised Learning and clustering. Required Materials: Textbook: Duda, Hart, and Stork, Pattern Classification, 2nd Edition, John Wiley, 2001. Auxiliary Textbooks: 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 Beio and Aaron Courville, Deep Learning, MIT Press, 2016. Technical Requirements: Prerequisites: Basic probability and statistics, some linear algebra, basic programming skills. Working familiarity with Matlab, Python, Java or C/C++ will be expected. View Catalog Entry
| ||||