CS 4780: Machine Learning

Cross-Listing: Not cross-listed.

Learning and classifying are two of our basic abilities. Machine learning is concerned with the question of how to train computers to learn from experience, to adapt and make decisions accordingly. This course will introduce the set of techniques and algorithms that constitute machine learning as of today, including inductive inference of decision trees, the parametric-based Bayesian learning approach, Bayesian belief networks and Hidden Markov Models, non-parametric methods, discriminent functions and support vector machines, neural networks, stochastic methods such as genetic algorithms, unsupervised learning and clustering, and other issues in the theory of machine learning. These techniques are used today to automate procedures that so far were performed by humans, as well as to explore untouched domains of science.

Offered: Fall only

Prerequisites: CS 2800, 3110, and basic knowledge of linear algebra and probability theory.

Grade options: Letter or S/U

Credit hours: 4

Recent offerings:

• http://www.cs.cornell.edu/courses/cs4780/2009fa
• http://www.cs.cornell.edu/courses/cs478/2008sp
  • Thorsten Joachims
  • Richard Caruana
• http://www.cs.cornell.edu/courses/cs478/2007sp
  • Thorsten Joachims