Introduction to Machine Learning

Identifier
COMS 5740
Professor(s)

Last Updated: Spring 2025

  1. Credits and contact hours: 3 credits, 4 contact hours
  2. Textbook, title, author, and year: None required
  3. Other supplemental materials: Learning from Data, Y.S. Abu-Mostafa, M. Magdon-Ismail, H. Lin; Machine Learning: A Probabilistic Perspective, K.P. Murphy; Deep Learning, I. Goodfellow, Y. Bengio, and A. Courville

Specific course information

  1. Brief description of the content of the course: Introduction to concepts, tools, and techniques of machine learning for applications. Selected machine learning algorithms in practical data mining tasks such as classification, regression, and clustering, e.g., association rules, decision trees, linear models, Bayesian learning, support vector machines, artificial neural networks, instance-based learning, probabilistic graphical models, ensemble learning, and clustering algorithms. Selected applications in data mining and pattern recognition.
  2. Prerequisites or co-requisites: COM S 3110, STAT 3050 or STAT 3300 or STAT 3410 or STAT 3470, MATH 1650, ENGL 2500
  3. Required, elective, or selected elective? Selected Elective

Specific goals for the course

Upon completing this course, students will be able to do the following:

  • Understand the fundamental concepts in machine learning, specifically within supervised, unsupervised, and deep learning
  • Ability decide the appropriateness of various machine learning methods for a given task
  • An understanding of how machine learning methods work, the principles behind their design, their underlying assumptions, and their limitations
  • Ability to apply machine learning methods to data and 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

  • Review of probability theory
  • Bayesian decision theory
  • Maximum-Likelihood and Bayesian parameter estimation
  • Evaluation of classifiers
  • Naïve Bays Classifier
  • Nearest neighbor methods
  • Linear models
  • Decision trees
  • Neural networks
  • Support vector machines
  • Bayesian networks
  • Ensemble classifiers
  • Unsupervised learning: Clustering
  • Student project presentations