Machine Learning

Course
Identifier: 
COM S 5730
Professor(s): 
Mengdi Huai

Last Updated: Fall 2024

  1. Credits and contact hours: 3 credits, 4 contact hours
  2. Instructor’s or course coordinator’s name: Mengdi Huai
  3. Text book, title, author, and year: None required
  4. Other supplemental materials:
    • Introduction to Machine Learning, 4th Edition, by Alpaydin
    • Deep Learning, Ian Goodfellow, by Yoshua Bengio and Aaron Courville
    • Learning from Data, by Yaser S. Abu-Mostafa, Malik Magdon-Ismail and Hsuan-Tien Lin
    • Machine Learning – A Probabilistic Perspective, by Kevin P. Murphy
    • Machine Learning – An Algorithmic Perspective, by Stephen Marsland

Specific course information

  1. Brief description of the content of the course: Basic principles, techniques, and applications of machine learning. Design, analysis, theoretical foundation, implementation, and applications of learning algorithms. Selected machine learning techniques in supervised learning, unsupervised learning, and reinforcement learning, including Bayesian decision theory, computational learning theory, decision trees, linear models, support vector machines, artificial neural networks, instance-based learning, probabilistic graphical models, ensemble learning, clustering algorithms, dimensionality reduction and feature selection. Selected applications in data mining and pattern recognition.
  2. Prerequisites or co-requisites: Graduate classification or Permission of Instructor

Specific goals for the course

  1. Specific outcomes of instruction:
  • Understanding of the fundamental concepts in machine learning, specifically within supervised, unsupervised, and deep learning.
  • Ability to decide the appropriateness of various machine learning methods for a given task.
  • An understanding of how machine learning methods work and the principles behind their design, their underlying assumptions, and their limitations.
  • Ability to apply machine learning methods to data and to evaluate their performance.
  • Familiarity with some current applications of machine learning.
  • Ability to communicate effectively about machine learning problems, algorithms, implementations, and their experimental evaluation.

Brief list of topics to be covered

  • machine learning problems
  • supervised
  • unsupervised
  • deep learning