Page 189 - University Bulletin

CS439 Search Engines and Information Retrieval
Credits: 3(3,0,1) Prerequisites: CS340
The course explores the basic and advanced techniques for extraction of information from search
engines. Items of interest relating to information retrieval examined in the course include: web
search engines; dictionaries and tolerant retrieval; indexing and invert indexing algorithms; index
construction and compressions; handling imprecise matching, ranking and relevance; and
machine learning and numerical methods in information retrieval, classification, clustering, web
search and challenges.

CS455 Computational Bioinformatics
Credits: 3(3,0,1) Prerequisites: CS311
This course presents an overview of important applications of computers to solve problems in
biology. The aim of the course is to introduce CS students to modern computational practices in
bioinformatics. Major topics covered are computational molecular biology (analysis of protein and
nucleic acid sequences), biological modeling and simulation (including computer models of
population dynamics, Bioinformatics databases, BLAST). The course concentrates on the
algorithmic details of bioinformatics.
CS460 Introduction to Robotics
Credits: 3(3,0,1) Prerequisites: CS210 and Instructor consent
The objective of this course is to present the fundamental concepts to develop autonomous
mobile robots. The course covers the basics of mobile robots control, kinematic theory,
navigation, localization and perception. The course will consolidate the understanding of
theoretical concepts through practical hands-on activities pertaining to robot programming and
deployment. The aim of this course is to give PSU students, in computer science and engineering
colleges, an opportunity to discover the world of robotics, and design and develop real robotic
applications.

CS465 Machine Learning
Credits: 3(3,0,1) Prerequisites: CS316
This course covers the theory and practice of machine learning from a variety of perspectives. It
explores topics such as learning decision trees, neural network learning, statistical learning
methods, genetic algorithms, Bayesian learning methods, explanation-based learning, and
reinforcement learning. Typical assignments include neural network learning for face recognition
and decision tree learning from databases of credit records.

CS469 Digital Image Processing
Credits: 3(3,0,1) Prerequisites: CS316
The course deals with image processing and its applications. Students learn the fundamental
concepts of visual perception and image acquisition, together with the basic techniques of image
manipulation, segmentation and coding, and a preliminary understanding of pattern recognition
and computer vision.
CS471 Data Mining
Credits: 3(3,0,1) Prerequisites: CS316
This course introduces Data Mining (DM). DM topics range from statistics to machine learning to
database, with a focus on analysis of large data sets. The course requires students to apply data
mining techniques in order to complete a project involving real data.

177 PSU UNDERGRADUATE BULLETIN