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.