CSE 6521
Transcript Abbreviation:
Adv Surv Art Int
Course Description:
Survey of advanced concepts, techniques, and applications of artificial intelligence, including knowledge representation, learning, natural language understanding, and vision.
Course Levels:
Graduate (5000-8000 level)
Designation:
Elective
General Education Course:
(N/A)
Cross-Listings:
(N/A)
Credit Hours (Minimum if “Range”selected):
3.00
Max Credit Hours:
3.00
Select if Repeatable:
Off
Maximum Repeatable Credits:
(N/A)
Total Completions Allowed:
(N/A)
Allow Multiple Enrollments in Term:
No
Course Length:
14 weeks (autumn or spring)
Off Campus:
Never
Campus Location:
Columbus
Instruction Modes:
In Person (75-100% campus; 0-24% online)
Hybrid Class (25-74% campus; 25-74% online)
Distance Learning (100% online)
Prerequisites and Co-requisites:
Prereq: 4256 or equiv., Math 4568 or equiv., and Stat 3470 or equiv.
Electronically Enforced:
No
Exclusions:
Not open to students with credit for 5521 or 5522.
Course Goals / Objectives:
Master advanced AI concepts, theories, and terminology
Master computational techniques in typical AI subareas
Master knowledge representation and reasoning methods in AI
Be exposed to current research topics in AI
Check if concurrence sought:
No
Contact Hours:
| Topic | LEC | REC out-of-class | REC in-class | Weekly LAB out-of-class | Weekly LAB in-class |
|---|---|---|---|---|---|
| Search and problem formation | 6.0 | 0.0 | 0 | 0.0 | 0 |
| Uncertainty, probability theory, and utility theory | 3.0 | 0.0 | 0 | 0.0 | 0 |
| Bayesian Networks | 3.0 | 0.0 | 0 | 0.0 | 0 |
| Expectation-Maximization Algorithm | 3.0 | 0.0 | 0 | 0.0 | 0 |
| Markov Models | 3.0 | 0.0 | 0 | 0.0 | 0 |
| Markov Decision Processes and Reinforcement Learning | 6.0 | 0.0 | 0 | 0.0 | 0 |
| Decision Trees and Ensemble Learning | 3.0 | 0.0 | 0 | 0.0 | 0 |
| Perceptrons and Neural Networks | 3.0 | 0.0 | 0 | 0.0 | 0 |
| Clustering | 1.5 | 0.0 | 0 | 0.0 | 0 |
| Application Areas (Natural Language Processing, Vision) | 4.5 | 0.0 | 0 | 0.0 | 0 |
| AI, Ethics, and Bias | 3.0 | 0.0 | 0 | 0.0 | 0 |
| AI Pedagogy | 0.0 | 0.0 | 0 | 0.0 | 0 |
| Total | 39 | 0 | 0 | 0 | 0 |
Grading Plan:
Letter Grade
Course Components:
Lecture
Grade Roster Component:
Lecture
Credit by Exam (EM):
No
Grades Breakdown:
| Aspect | Percent |
|---|---|
| Quizzes | 10% |
| Pencil and Paper Homeworks | 10% |
| Programming Assignments | 10% |
| Group Final Project | 20% |
| Pedagogy Project | 20% |
| Ethics Project | 20% |
| Participation | 10% |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| Artificial Intelligence: A Modern Approach, 4rd edition | Stuart Russell and Peter Norvig |
ABET-CAC Criterion 3 Outcomes:
(N/A)
ABET-ETAC Criterion 3 Outcomes:
(N/A)
ABET-EAC Criterion 3 Outcomes:
(N/A)
Embedded Literacies Info: