### Current Catalog Description

An introductory course offers a broad overview of the main techniques in machine learning. Students will study the basic concepts of advanced machine learning methods as well as their theoretical background. Topics of learning theory (bias/variance tradeoffs; VC theory); supervised learning parametric/nonparametric methods, Bayesian models, support vector machines, neural networks); unsupervised learning (dimensionality reduction, kernel tricks, clustering) and reinforcement learning will be covered. Prerequisites: (CSE 002 or CSE 012) and (Math 205 or Math 43) and (Math 231 or ISE 121 or ECO 045)

### Instructor: Sihong Xie (Spring 2019)

### Textbooks:

Shai Shalev-Shwartz and Shai Ben-David, "Understanding Machine Learning: From Theory to Algorithms", 1st Edition, Cambridge University Press, 2014, ISBN 978-1107057135 (required)

Christopher M. Bishop, "Pattern Recognition and Machine Learning", Springer, 2011, ISBN 978-0387310732 (required)

Kevin P. Murphy, "Machine Learning: A Probabilistic Perspective", The MIT Press, 2012, ISBN 978-0262018029 (optional)

### COURSE OUTCOMES

### Students will have

### RELATIONSHIP BETWEEN COURSE OUTCOMES AND STUDENT ENABLED CHARACTERISTICS

### CSE 326 substantially supports the following student enabled characteristics

A. An ability to apply knowledge of computing and mathematics appropriate to the discipline

B. An ability to analyze a problem and identify and define the computing requirements appropriate to its solution

I. An ability to use current techniques, skills, and tools necessary for computing practices

J. An ability to apply mathematical foundations, algorithmic principles, and computer science theory in the modeling and design of computer-based systems in a way that demonstrates comprehension of the tradeoffs involved in design choices

K. An ability to apply design and development principles in the construction of software systems of varying complexity

### Major Topics Covered in the Course

- Decision trees, naive Bayes, logistic and linear regression.
- Learning theory: Bias/variance tradeoff, PAC learning, VC dimension.
- Convex optimization
- Large margin methods: boosting, perceptron, SVM and kernel.
- Probabilistic graphical models: Markov random fields, Bayesian networks and probabilistic inference.
- Clustering: K-means, Gaussian mixture models, EM algorithm
- Data dimensionality reduction: PCA, ICA
- Neural network: back-propagation, convolutional/recurrent neural networks.
- Reinforcement learning.