Mathematics, Statistics & Computer Science
University of Wisconsin-Stout
Jarvis Hall Science Wing 231
Menomonie, WI 54751-0790
COURSE NUMBER/TITLE:
CS-354 ALGORITHMS AND ARTIFICIAL INTELLIGENCE
CREDITS:
3
COURSE DESCRIPTION:
Introduction to the design and analysis of computer algorithms.
Concepts of artificial intelligence and machine learning to enhance
analytical and problem solving skills. Prerequisite CS-244 Data
Structures
TEXTBOOK: Introduction Algorithms, 2nd Ed., by Cormen (adopted Fall 2010)
COURSE OBJECTIVES:
Upon successful completion of the course, the student will be able to:
1. Analyze the performance of computer algorithms.
2. Understand programming techniques and data structures used in the implementation of effective
algorithms.
3. Implement efficient algorithms from pseudo code using computer programming skills.
4. Understand the concept of NP-completeness theory.
5. Understand the basic principles of artificial intelligence, machine learning and data mining.
6. Apply and implement related techniques to computational data analysis.
7. Solve analytical problems in various areas of computer science.
COURSE OUTLINE:
1. Analysis of algorithms (Objectives 1, 2, 3, 4, 7)
a. Fundamentals: specification, runtime of an algorithm
b. Growth of functions: Big-O, Theta, Omega notations
c. Divide and conquer techniques, i.e. quick sort
d. Recurrences: solution using iterative method and Master’s theorem
e. Greedy algorithms: activity selection, minimal spanning tree-traversal techniques
f. Dynamic programming: properties, problems on Longest Common Subsequence, Knapsack,
string matching: local and global alignment
g. NP-completeness: definition and getting familiar with some well-known problems
2. Artificial Intelligence and Machine Learning (Objectives 5, 6, 7)
a. Introduction: learning theory
b. Agent and search methods, game playing
c. Tree walk algorithms: binary search tree, breadth first search, depth first search
d. Logical reasoning and first order logic
e. Pattern recognition in data analysis
f. Classification techniques: decision tree, neural networks, naïve Bayes theorem, k-Nearest
Neighbor
Updated 8/2015
2/2011