Term: Winter 2010
Course No.: EECS 498 - 004
Credit Hours: 3
Instructor: Satinder Singh Baveja
Prerequisites: EECS 281 or Consent of Instructor
The course is a programming-focused introduction to Machine Learning. Increasingly, extracting value from data is an important contributor to the global economy across a range of industries. The field of Machine Learning provides the theoretical underpinnings for data-analysis as well as more broadly for modern artificial intelligence approaches to building artificial agents that interact with data.
In this course, students will learn about all three subareas of Machine Learning: 1) Supervised learning (approaches to regression and classification), 2) Unsupervised learning (approaches to density estimation, and clustering/dimensionality reduction), and 3) Reinforcement Learning (approaches to sequential decision-making). The course will emphasize understanding the foundational algorithms and tricks of the trade through implementation and basic-theoretical analysis. Real data sets will be used whenever feasible to encourage understanding of practical issues.
Students will be expected to program in Matlab as well as in one of C/C++/Java (students will have a choice).
Related Topics: Lab-Artificial Intelligence