CSE 326 Foundations of Machine Learning (3)

Instructor

Miaomiao Zhang (Spring 2018)

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)

Textbooks:

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

Mehryar Mohri, Afshin Rostamizadeh and Ameet Talwalkar, "Foundations of Machine Learning (Adaptive Computation and Machine Learning series), The MIT Press, 2012, ISBN 978-0262018258

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

  • Regression methods: logitstic regression, perceptron, exponential family
  • Generative learning algorithms, Gaussian discriminant analysis, naive Bayes, SVM
  • Bias/variance tradeoff
  • Model selection and feature selection
  • Convex optimization
  • Clustering: K-means, Gaussian mixture models, EM algorithm 
  • Data dimensionality reduction: PCA, ICA
  • Neural network and reinforcement learning
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