## 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:

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

### 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