CSE103 – A Practical Introduction to Probability and Statistics
Units: 4
Course Objectives:
CSE 103 can be used as an alternative to Math 183. CSE 103 is not duplicate of
ECE 109, ECON 120A or Math 183. Traditionally,
computer algorithms have been designed to correctly process any input from a set of
allowable inputs. This is reflected in
the emphasis that computer science education places on logic, discrete
math and worst-case analysis. On the other hand, the actual performance of
computers in terms of speed, memory and power consumption, and increasingly
also correctness, depends on the distribution of the data it receives as input.
It is becoming critically important for software and hardware developers to employ
statistical methods in the design and analysis of the systems that they develop.
This need is most apparent in areas such as computer vision, machine learning and
bio-informatics. It is also becoming increasingly important in traditional areas
of computer science such as communication protocols, memory management, computer
architecture and databases.
Course Description:
Distribution over the real line. Independence, expectation, conditional expectation, mean, variance,
Hypothesis testing. Learning classifiers. Distributions over R^n, covariance matrix, Binomial, Poisson
distributions. Chernoff bound. Entrophy. Compression. Arithmetics coding. Maximal likelihood estimation.
Bayesian estimation.
Format:
Per week: 3 hours of lecture, 1 hour discussion section, 6 hours of computer
lab, and 2 hours of outside preparation.
Prerequisites:
Math 20A, Math 20B and Math 20F, or consent of the instructor.
Other restrictions:
Example Textbook(s):
Laboratory work:
Offered:
Normally Fall.
Resources
