Spring 2019 not offered
The content of this course will be artificial intelligence and machine learning. The course will cover search strategies and planning and will build up to basic machine learning principles and techniques. Includes some programming.
||Gen Ed Area Dept:
|Course Format: Lecture||Grading Mode: Graded|
||Prerequisites: MATH228 AND COMP212
||Fulfills a Major Requirement for: (COMP)(MATH)(NS&B)
Trevor Hastie, Robert Tibshirani and Jerome Friedman, THE ELEMENTS OF STATISTICAL LEARNING: DATA MINING, INFERENCE AND PREDICTION
Kevin Murphy, MACHINE LEARNING A PROBABILISTIC PERSPECTIVE
|Examination and Assignments: |
1 midterm and 1 final. Homework assignments roughly once every 2 weeks. They will each include one programming question and a few theory questions.