Introduction to Machine Learning

10-401, Spring 2018

Carnegie Mellon University

Maria-Florina Balcan


Monday and Wednesday from 10:30-11:50am (NSH 3002)


Tuesdays 7-8pm Baker Hall 235A

Piazza Webpage:

Course Description:

Machine Learning is concerned with computer programs that automatically improve their performance through experience (e.g., programs that learn to recognize human faces, recommend music and movies, and drive autonomous robots). This course covers the theory and practical algorithms for machine learning from a variety of perspectives. We cover topics such as decision tree learning, Support Vector Machines, neural networks, boosting, statistical learning methods, unsupervised learning, active leaerning, and reinforcement learning. Short programming assignments include hands-on experiments with various learning algorithms.

  • Machine Learning, Tom Mitchell. (optional)
  • Pattern Recognition and Machine Learning, Christopher Bishop. (optional)
  • Machine Learning: A Probabilistic Perspective, Kevin P. Murphy, available online, (optional)
  • Homeworks (40%). There are five and you can drop one.
  • Midterm (20%)
  • Final (20%)
  • Project (15%)
  • Class Participation (5%)