Introduction to Machine Learning

10-315, Spring 2019

Carnegie Mellon University

Maria-Florina Balcan


Monday and Friday from 12:00-1:20pm (BH A51)


Thursdays from 7:00 to 8:30 pm in DH 2315

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. available online , (optional)
  • Machine Learning: A Probabilistic Perspective, Kevin P. Murphy, available online, (optional)
  • Homeworks (40%). There are six or seven.
  • Midterm (25%)
  • Final (30%)
  • Class Participation (5%)