Computer Science @ UC Davis | Course Descriptions

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

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:

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
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
Application of Markov Models
1. Markov chains
2. Hidden Markov models
3. Queuing models
4. Markov Chain Monte Carlo
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

Assessment Plan for the Course Outcome	How Measured	When Measured
The ability to apply knowledge of basic science, mathematics and engineering principles to solve computing and information processing problems	Project	Once a year
The ability to write correct and good programs	Graded homework	Once a year
The ability to effectively express ideas through written communication	Final written report	Once a year
The ability to write correct and good programs	Exam problems include simulation programming	Once a year
The ability to understand the implications of contemporary computing and information processing issues relative to society	Implications for society discussed often in class, covered in final report	Once a year

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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