CSE 103 - A Practical Introduction to Probability and Statistics



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.

Course Objectives: 

  • CSE 103 can be used as an alternative to Math 183.
  • CSE 103 is not a 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.


Math 20A and Math 20B with a passing grade of C- or better. Please see Prerequisites Page


Normally Fall.