Fall 2020 - CSE 353

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 @ B103 or online CSE353 Zoom meeting
  • Office Hours: Tue & Thu 10:30-12:30 pm in B424 or online Zoom meeting
  • Course Homepage: http://ppawar.github.io/Fall2020/CSE353-F20/index.html
  • Teaching assistant (TA): To be decided

Course Description

Covers fundamental concepts for intelligent systems that autonomously learn to perform a task and improve with experience, including problem formulations (e.g., selecting input features and outputs) and learning frameworks (e.g., supervised vs. unsupervised), standard models, methods, computational tools, algorithms and modern techniques, as well as methodologies to evaluate learning ability and to automatically select optimal models. Applications to areas such as computer vision (e.g., character and digit recognition), natural-language processing (e.g., spam filtering) and robotics (e.g., navigating complex environments) will motivate the coursework and material.

Prerequisite

  • CSE 216 or CSE 219 or CSE 260; CSE major
  • Pre- or Co-requisite: AMS 310 or AMS 311 or AMS 312

Required Texts

  • (Textbook 1) 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.
  • (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.

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) Aston Zhang et. al., Dive into Deep Learning. Open source book available at http://d2l.ai, Aug 2020