ECS 256, Winter Quarter 2014

Prof. Dipak Ghosal and I are doing a major revamp of ECS 256. The formal change process may take a while, but I will be teaching 256 next quarter (Winter 2014) according to the new version of the course.

Details are given below. Let me know if you have any questions.

Norm Matloff

ECS 256

Title: Probablistic Modeling in Computer Science

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

Catalog Description: Probabilistic and statistical models useful in data science. Applications to networks, business, bioinformatics, data mining, machine learning, software engineering, algorithms and so on. Advanced R programming.

Grading: Letter grade. Based on group homework, with interactive grading. No exams.

Textbook: N. Matloff, From Algorithms to Z Scores: Probablistic and Statistical Modeling in Computer Science (open source, available online); coverage of Chapters 6, 8, 10, 15, 16, 18, 19, 21.

Topical Coverage: