ECS 256, Winter Quarter 2016

Probabilistic Modeling (NOT Performance Evaluation)

(There has been a name change planned for this course for a long time. Somehow it hasn't been processed yet.)

Professor: Norm Matloff

ECS 256

Title: Probablistic Modeling

Prerequisites: Calculus-based undergraduate course in probability modeling, such as ECS 132 or STA 131A; knowledge of matrix algebra, as in MAT 22A.

We will program in R. It is NOT assumed that you know R before taking the course; you will pick it up on your own if you don't have this background. Appendix A in the textbook gives a quick introduction to R.

Grading: Grade based mainly on homework, but there will also be two quizzes.

Topical Coverage: Markov chains; random vectors and matrix-valued analysis; mixture models, including EM algorithm; prediction models (regression, classification, machine learning).

Textbook: N. Matloff, From Algorithms to Z Scores: Probablistic and Statistical Modeling in Computer Science (open source, available online), covering parts of Chapters 6, 9, 11-13, 18, 19, 21-24

Workload:

Go here for further details.