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ECS 132 PROBABILITY AND STATISTICAL MODELING FOR COMPUTER SCIENCE (4) II

New course approved effective Winter 2009

Lecture: 3 hours

Laboratory: 1 hour

Prerequisites:Courses 50 or Engineering Electrical and Computer 70; course 60; Mathematics 21C; Mathematics 22A or Mathematics 67

Catalog Description:
Univariate and multivariate distributions. Estimation and model building. Markov/Hidden Markov models. Applications to data mining, networks, security, software engineering and bioinformatics.

Grading: Letter; 2 midterms (20% each), quizzes (20%), homework (20%), final (20%)

Expanded Course Description:

  1. Univariate and Multivariate Distributions
    1. Probability mass, density, and cumulative distribution functions
    2. Parametric families of distributions
    3. Expected value, variance, conditional expectation
    4. Applications of the univariate and multivariate Central Limit Theorem
    5. Probabilistic inequalities
  2. Sampling, Estimation and Modeling Building
    1. Random samples, sampling distributions of estimators
    2. Methods of Moments and Maximum Likelihood
    3. Statistical inference
    4. Introduction to multivariate statistical models: regression and classification problems, Log-linear model, principal components analysis
    5. The problem of overfitting; model assessment
  3. Application of Markov Models
    1. Markov chains
    2. Hidden Markov models
    3. Queuing models
    4. Markov Chain Monte Carlo
  4. Computer science and engineering applications (interspersed with the above topics throughout the course)
    1. Data mining
    2. Network protocols, analysis of Web traffic
    3. Computer security
    4. Software engineering
    5. Computer architecture, operating systems, distributed systems
    6. Bioinformatics

Textbooks:
Possible choices include: K.S. Trivedi, Probability and Statistics with Reliability, Queuing, and Computer Science Applications, Wiley, New York, 2001; M. Mitzenmacher, E. Upfal, Probability and Computing: Randomized Algorithms and Probabilistic Analysis, Cambridge, 2005; N. Matloff, A Course in Probabilistic and Statistical Modeling in Computer Science http://heather.cs.ucdavis.edu/~matloff/132/PLN

Computer Usage:
Moderately extensive programming, platform-independent, using the open-source programming language R or the MATLAB package.

ABET Category Content:

Engineering Science: 3 units
Engineering Design: 1 unit

Goals:

Students will:
  • understand basic notions of discrete and continuous probability
  • understand the philosophy behind basic methods of statistical inference, and the role that sampling distributions play in those methods
  • be able to perform correct and meaningful statistical analyses of simple to moderate complexity
  • have a first-level understanding of Monte Carlo simulation

Instructors: N. Matloff, D. Ghosal, I. Davidson

Prepared by: N. Matloff (December 2007)

Overlap Statement:
There is some topical overlap with MAT 135A and STA 131ABC, as well as with application-specific probability/statistics courses such as ECI 114, EEC 161, ECN 140 and so on. This course differs greatly in its collection of topics, its usage of computers, and especially in its computer science applications.

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