Course Information

CSCI 446
Artificial Intelligence
Fall 2015

Schedule | Assignments | Course syllabus | Moodle

COURSE SYLLABUS

Description:

An introduction to the basic concepts of Artificial Intelligence. Topics to be covered include the history of AI, the problems treated in AI, solution techniques, state spaces, search algorithms and heuristics, expert systems, natural language processing, and robotics. Prerequisite: CSCI 332.

Instructor:

Michele Van Dyne
mvandyne@mtech.edu
(406) 496-4855
Museum 204B (2nd floor, behind and to the left of Tami's office)
Office hours: Mon., Wed, Fri. 1:00-2:00, or by appointment.

Classes:

Mon., Wed., Fri.

9:00 - 10:00am

CBB 105

Resources:	Textbook	Artificial Intelligence: A Modern Approach (3rd edition) by Russell and Norvig
	Class web page	http://katie.mtech.edu/classes/csci446/

Evaluation:	Midterm Exams (2)	20%
	Final Exam	20%
	Assignments	40%
	Project	20%
	Staff discretion (participation, extra-credit, contests)	±?%

General:
Any student who may need an accommodation due to a disability, please make an appointment to see me during my office hours. A letter from a Montana Tech Disability Coordinator authorizing your accommodations is needed.

Expectations:

E1. Students should have a thorough understanding of space and time complexity of data structures and algorithms. (CSCI 332)
E2. Students should have a thorough understanding of recursion and recursive problem solving techniques. (CSCI 232)
E3. Students should have a thorough understanding of list structures and the algorithms associated with them. (CSCI 232)
E4. Students should have a thorough understanding of graphs, trees, and the algorithms associated with them. (CSCI 332)
E5. Students should have a working knowledge of logic and logical methods, including propositional and predicate calculus. (CSCI 246)

Course Outcomes:

R1. Students know the historical background of the field of Artificial Intelligence. (CAC-h)
R2. Students are aware of the relevant ethical considerations in the field of Artificial Intelligence. (CAC-e, g)
R3. Students understand and define the concept of a problem space for a problem. (CAC-a, j)
R4. Students can describe and implement brute-force searching techniques, such as breadth-first, depth-first, and iterative deepening. (CAC-a, b, c, j)
R5. Students can describe and implement best-first searching techniques such as best-first, Dijkstra's algorithm, and A*. (CAC-a, b, c, j)
R6. Students can describe and implement gaming search techniques such as minimax and alpha-beta pruning. (CAC-a, b, c, j)
R7. Students can describe and implement constraint satisfaction techniques such as backtracking and local search methods. (CAC-a,b, c, j)
R8. Students understand the use of logic (propositional and predicate calculus) as means of representing knowledge in a computer system. (CAC-a, b, c, j)
R9. Students can perform theorem proving using resolution in a logical knowledge-based system. (CAC-a, b, c, j)
R10. Students understand the concept of probabilistic reasoning and can determine when to use this concept. (CAC-a, b, c, j)
R11. Students are able to apply Bayes theorem to determine conditional probabilities. (CAC-a, b, c, j)
R12. Students have researched topics in Artificial Intelligence not covered in the lectures. (CAC-h)

Page last updated: August 23, 2015