Syllabus

Course objectives

By the end of this course, you will:

  1. Understand the fundamental principles of machine learning: You’ll grasp the core concepts of supervised and unsupervised learning, regression, classification, clustering, and more.
  2. Apply machine learning to real-world problems: You’ll tackle projects that explore how machine learning is used in diverse fields like healthcare, finance, marketing, and technology.
  3. Critically evaluate machine learning applications: You’ll develop the ability to discern the strengths and limitations of machine learning models, understanding the ethical considerations and potential biases involved.

Books, articles, and other materials

  • Ethem Alpaydin, Machine Learning: The New AI (Cambridge, Massachusetts: MIT Press, 2016).

  • Tom M. Mitchell, Machine Learning (New York, New York: McGraw Hill, 1997).

  • Kevin P. Murphy, Machine Learning: a Probabilistic Perspective (Cambridge, Massachusetts: MIT Press, 2012).

  • Andrew Ng, YouTube lecture series on machine learning

There will occasionally be additional articles and videos to read and watch. When this happens, links to these other resources will be included on the lecture page for that session.