Fall 2020 - CSE 512

Machine Learning

Syllabus

Basic Information

  • Term: Fall 2020
  • Instructor: Pravin Pawar (pravin.pawar@sunykorea.ac.kr, Office B424, +82-32-626-1227, +82-10-8692-4908)
  • Lectures: Mon & Wed 2:00-3:20 pm
  • Office Hours: Tue & Thu 10:30-12:30 pm in B424 or by appointment. Zoom meeting invitation: https://stonybrook.zoom.us/j/4312768560
  • Course Homepage: http://ppawar.github.io/Fall2020/CSE512-F20/index.html
  • Teaching assistant (TA): To be decided

Course Description

Machine Learning is centered around automated methods that improve their own performance through learning patterns in data, and then use the uncovered patterns to predict the future and make decisions. Examples include document/image/handwriting classification, spam filtering, face/speech recognition, medical decision making, robot navigation, to name a few. See this for an extended introduction. This course covers the theory and practical algorithms for machine learning from a variety of perspectives. The topics include Bayesian networks, decision tree learning, Support Vector Machines, statistical learning methods and unsupervised learning, as well as theoretical concepts such as the PAC learning framework, margin-based learning, and VC dimension. Short programming assignments include hands-on experiments with various learning algorithms, and a larger course project gives students a chance to dig into an area of their choice. See the syllabus for more. This course is designed to give a graduate-level student a thorough grounding in the methodologies, technologies, mathematics and algorithms currently needed by people who do research in machine learning.

Prerequisite

  • Limited to CSE graduate students; others, permission of instructor

Required Texts

  • Christopher M. Bishop, "Pattern Recognition and Machine Learning". Springer, 2011. ISBN: 0387310738.
  • (Textbook 2) Ian H. Witten, Frank Eibe, Mark A. Hall, and Christopher J. Pal. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, 2016.
  • (Textbook 3) Aurélien Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (2nd Edition) O'Reilly Media, Oct. 2019, ISBN-10: 1492032646.

Reference Texts

  • (Reference book 1) Jake VanderPlas. Python Data Science Handbook: Essential Tools for Working with Data. Shroff/O'Reilly Media, Inc., 2016.
  • (Reference book 2) Andriy Burkov, The Hundred-Page Machine Learning Book, January 2019, ISBN-10: 199957950X