ECS 271: Machine Learning & Discovery

Subject
ECS 271
Title
Machine Learning & Discovery
Status
Active
Units
4.0
Effective Term
2016 Fall Quarter
Learning Activities
Lecture: 3 hours
Project (Term Project) - 1.0 hours
Description
Artificial intelligence techniques for knowledge acquisition by computers. Fundamental problems in machine learning and discovery. Systems that learn from examples, analogies, and solved problems. Systems that discover numerical laws and qualitative relationships. Projects centering on implementation and evaluation.
Prerequisites
ECS 170
Enrollment Restrictions
Pass One and Pass Two open to Graduate Students in Computer Science only.

Summary of Course Content

  1. Overview; claims; ways to evaluate learning and discovery systems
  2. Inductive learning (learning conjunctive concepts from examples)
  3. Learning decision trees
  4. Conceptual clustering
  5. Learning and discovery of heuristics for problem-solving
  6. Discovery of numerical laws
  7. Learning from analogies, learning from experiments
  8. Deductive learning (explanation-based learning and the connection to program optimization)

Illustrative Reading
T.M. Mitchell, Machine Learning, McGrawHill, 1997

Potential Course Overlap
This course does not have a significant overlap with any other course. It covers some of the topics as in EEC 207, but does so at a more software-related level. Applications in computational science are emphasized throughout.

Course Category

Graduate