This introductory course on machine learning will give an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as classification and linear regression and ending up with more recent topics such as boosting, support vector machines, hidden Markov models, and Bayesian networks. The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work. The underlying theme in the course is statistical inference as it provides the foundation for most of the methods covered.

Lectures: Mon and Wed 1-2.30pm in 54-100

Instructors:

• Prof. Tommi Jaakkola (tommi at csail dot mit dot edu), office hours: Mon 3-4pm, 32-G498
  
• Prof. Michael Collins (mcollins at csail dot mit dot edu), office hours: Wed 3-4pm, 32-G484

Teaching assistants:

• David Sontag (dsontag at mit dot edu), office hours: Tue 1-2pm, 32-G4 lounge
• Hung-An Chang (hungan_c at mit dot edu), office hours: Thu 4-5pm, 32-G4 lounge
• Zoran Dzunic (zoki at mit dot edu), office hours: Thu 3-4pm, 32-G4 lounge

• Bishop, "Pattern Recognition and Machine Learning", 2007
• Duda, Hart, Stork, "Pattern Classification", 2000
• Hastie, Tibshirani and Friedman, "Elements of Statistical Learning: Data Mining, Inference and Prediction", 2001
• MacKay, "Information Theory, Inference, and Learning Algorithms", 2003.
  Available on-line here
• Mitchell, "Machine Learning", 1997.