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:

• Classification and linear regression
• Support vector machines
• Ensemble methods, boosting algorithms
• Learning theory: bias-variance, uniform convergence, VC dimension
• Mixtures models, EM algorithm and hidden Markov models
• Structured prediction

Lectures

Time: Monday/Wednesday 4:10 pm -5:25 pm; Location: Joshi 193

Instructor

Shaojun Wang
387, Joshi Center
shaojun.wang(at)wright.edu
(937) 775-5140
Office hours: Monday/Wednesday 2:30PM-3:30PM

Textbook

Bishop, C.
Pattern Recognition and Machine Learning
Springer, 2006.

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

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

Course Grades and Workload

Three Homeworks 60%
Project or Final Exam 40%

Prerequisites

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