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 http://heather.cs.ucdavis.edu/matloff.html 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, 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, http://heather.cs.ucdavis.edu/probstatbook Coverage of Chapters 6, 8, 10, 15, 16, 18, 19, 21. Topical Coverage: 1. Review of undergrad probability (densities, properties of expected value and variance, famous discrete and continuous distribution families) and of R programming. 2. Discrete- and continuous-time Markov chains; Hidden Markov models; Markov Chain Monte Carlo; basic queuing models. Discrete-event simulation. 3. Hazard functions; with applications to system reliability, including software engineering. 4. Prediction, especially with nonparametric (machine learning) methods. 5. Statistical issues with Big Data. 6. Advanced R programming, especially regarding high-performance R.