ECE595: Machine Learning

Lecture Hours: 3 

Credits: 3

Area of Specialization: CNSIP

Catalog Description: An introductory course to machine learning, with a focus on supervised learning using linear models. The course will have four parts: (1) mathematical background on linear algebra, probability, and optimization. (2) classification methods including Bayesian decision, linear regression, logistic, regression, and support vector machine. (3) robustness of classifier and adversarial examples. (4) learning theory on the feasibility of learning, VC dimension, complexity analysis, bias-variance analysis. Suitable for senior undergraduates and graduates with a background in probability, linear algebra, and programming.

Learning Outcomes:

• Explain what a linear model is, such as Bayesian decision rule, perceptron algorithm, logistic regression, support vector machine, etc.
• Describe a linear classifier from a geometric perspective.
• Describe how to attack a classifier.
• Explain what a machine learning algorithm can do, and what a machine learning algorithm cannot do.
• Implement machine learning algorithms using Python and CVX.

Required Text(s): None

Recommended References:

• Pattern Classification, by Duda, Hart and Stork, Wiley-Interscience; 2^nd edition, 2000. Available in full-text online from Purdue Libraries.
• Learning from Data, by Abu-Mostafa, Magdon-Ismail and Lin, AMLBook, 2012.
• Elements of Statistical Learning, by Hastie, Tibshirani and Friedman, Springer, 2^nd edition, 2009. Available in full-text online from Purdue Libraries.
• Pattern Recognition and Machine Learning, by Bishop, Springer, 2006.

Prerequisites: Students should have good knowledge of probability, linear algebra, and optimization. Course will also require Python program and CVX.