CIS 2033: Computational Probability and Statistics, Spring 2013

Lectures: Tuesday and Thursday 9:30-10:50  TL 403B

Lab: Wednesday 13:00-14:50  Wachman 104


Schedule and Homework Assignments


Matlab Assignments





The goal is to introduce students to hot and extremely useful topics in computational statistics with hands on experience. It provides a modern approach to probability and computational statistics with applications in data mining. Students will be able to immediately see their results with programming assignments in Matlab. Matlab is a leading programming language of scientific computing. It is broadly used in the industry and academia. No prior Matlab knowledge is required. The course offers a solid foundation for further courses in data mining, machine learning, artificial intelligence, robotics, computer vision, and in general in computational statistics and scientific computing. The course is composed of 3 hours lecture and 2 hours lab with programming assignments in Matlab.

Course books:

Michael Baron. Probability and Statistics for Computer Scientists, CRC, 2006, ISBN-10: 1584886412

Dekking, F.M., Kraaikamp, C., Lopuhaa, H.P., Meester, L.E., A Modern Introduction to Probability and Statistics. Second Edition. Springer 2007

ISBN: 978-1-85233-896-1



Also recommended but not required are:

Wendy L. Martinez and Angel R. Martinez. Computational Statistics Handbook with Matlab. Second Edition. CRC 2008.

Daniel T. Kaplan. Introduction to Scientific Computation and Programming. Thomson 2004.

Course topics:

  1. Introduction
  2. Probability Concepts
  3. Sampling Concepts
  4. Generating Random Variables
  5. Exploratory Data Analysis
  6. Finding Structure in Data
  7. Monte Carlo Methods in Inferential Statistics
  8. Data Partitioning
  9. Probability Density Estimation
  10. Supervised Learning
  11. Unsupervised Learning
  12. Parametric Models
  13. Nonparametric Models
  14. Markov Chain Monte Carlo Methods


CIS 1068 (or CIS 1073) and Math 1041 (or Math 1031) with grades of C or better


Questions, email:



Maximum Likelihood Estimation Primer

Hidden Markov Model (HMM)

Viterbi Algorithm for HMM

Video Lectures by Sam Roweis



Exams, Project and Grading

Honor Code


I encourage students with disabilities, including "invisible" disabilities such as chronic diseases and learning disabilities, to discuss with us any appropriate accommodations that we might make on their behalf. Student must provide me with a note from the office of Disability Resources and Services at in 100 Ritter Annex, 215-204-1280, regarding their disability.