Computational Probability and Statistics

Course number: 
CIS 2033
Semester: 
Fall 2016
Instructors:
Name E-mail Office location
Pei Wang pei.wang@temple.edu
Science Education and Research Center (SERC), Room 347
Richard Beigel richard.beigel@temple.edu
Science Education and Research Center (SERC) , Room 312
David Dobor 2dave@temple.edu
Science Education and Research Center (SERC), Room 315
Prerequisites: 

(  Course or Test: CIS 1068 | Minimum Grade of C- | May not be taken concurrently.  )
OR
(  Course or Test: CIS 1073 | Minimum Grade of C- | May not be taken concurrently.  )
AND ( Course or Test: CIS 1166 | Minimum Grade of C- | May not be taken concurrently.  )
AND ( Course or Test: MATH 1041 | Minimum Grade of C- | May not be taken concurrently.  )
OR
(  Course or Test: MATH 1022 | Minimum Grade of C- | May not be taken concurrently.
AND Course or Test: MATH 1031 | Minimum Grade of C- | May not be taken concurrently.  )
OR
(  Course or Test: MATH 1941 | Minimum Grade of C- | May not be taken concurrently.  )
OR
(  Course or Test: MATH 1042 | Minimum Grade of C- | May be taken concurrently.  )
OR
(  Course or Test: MATH 1942 | Minimum Grade of C- | May be taken concurrently.  )
OR
(  Course or Test: MATH 1951 | Minimum Grade of C- | May be taken concurrently.  )
OR
(  Course or Test: MATH 2043 to 3080 | Required Courses: 1 | Minimum Grade of C- | May be taken concurrently.  )
OR
(  MA05 Y - May not be taken concurrently.  )
OR
(  MA06 Y - May not be taken concurrently. )

Textbooks: 
Required Textbook: Probability and Statistics for Computer Scientists, Second Edition, by Michael Baron, Chapman and Hall/CRC 2013, ISBN: 978-1-4398-7590-2
Reference Textbook: A Modern Introduction to Probability and Statistics, by Dekking, F.M., Kraaikamp, C., Lopuhaa, H.P., Meester, L.E. Springer 2007, ISBN: 978-1-85233-896-1
Topics covered: 
Probability, Discrete random variables, Continuous distributions, Simulation of random variables, Introduction to statistics, Parameter estimation, Least squares estimation
Course goals: 
The goal is to introduce students to modern and extremely useful topics in computational statistics. It focuses on computational aspects and provides a hands-on introduction to fundamental concepts of data analysis. The course is composed of 3 hours lecture and 2 hours lab with programming assignments in Matlab. Consequently, the students will be able to immediately see their results with programming assignments in Matlab. 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.
Attendance policy: 
Attendance to all meetings of the class is mandatory
Accomodations for Students with Disabilities: 
Any student who has a need for accommodation based on the impact of a documented disability, including special accommodations for access to technology resources and electronic instructional materials required for the course, should contact me privately to discuss the specific situation by the end of the second week of classes or as soon as practical. If you have not done so already, please contact Disability Resources and Services (DRS) at 215-204-1280 in 100 Ritter Annex to learn more about the resources available to you. I will work with DRS to coordinate reasonable accommodations for all students with documented disabilities. (http://www.temple.edu/studentaffairs/disability/accommodations/).
Student and Faculty Academic Rights and Responsibilities: 
Freedom to teach and freedom to learn are inseparable facets of academic freedom. The University has a policy on Student and Faculty and Academic Rights and Responsibilities (Policy #03.70.02) which can be accessed through the following http://policies.temple.edu/PDF/99.pdf.