CS714: Machine Learning
Fall 2013

Summary

This introductory course on machine learning will give an overview of many concepts, techniques, and algorithms in machine learning that are now widely applied in scientific data analysis, data mining, trainable recognition systems, adaptive resource allocators, and adaptive controllers. The emphasis will be on understanding the fundamental principles that permit effective learning in these systems, realizing their inherent limitations, and exploring the latest advanced techniques employed in machine learning.

Topics include:

Lectures

Time: Monday/Wednesday 4:40 pm -6:00 pm; Location: Russ 155

Instructor

Shaojun Wang
387, Joshi Center
shaojun.wang(at)wright.edu
(937) 775-5140
Office hours: Monday/Wednesday 3:30PM-4:30PM

Textbook

K. Murphy
Machine Learning: A Probabilistic Perspective
MIT Press, 2012.

T. Hastie, R. Tibshirani and J. Friedman
The Elements of Statistical Learning: Data Mining, Inference and Prediction
Springer, 2nd Edition, 2009.

V. Vapnik
The Nature of Statistical Learning Theory
Springer, 2nd Edition, 2000.

Course Grades and Workload

Four Homeworks 70%
Project or Final Exam 30%

Prerequisites

Probability and Statistics
Linear Algebra
Optimization
Programming language: matlab, C++, Java