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
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.