This course provides opportunities to learn the elements and techniques of artificial intelligence and how they apply to daily life. Concepts and methods are illustrated with real-world applications.
This course is designed for upper-level computer science undergraduate and first-year graduate students. Artificial intelligence (AI) has a long history in computer science but continues to grow in part due to new applications in diverse fields. The frontier areas of AI include autonomous vehicles, face recognition, speech recognition, robotics, and many more. The aim of this course is to provide foundational knowledge in the field of artificial intelligence. This course assumes no previous knowledge of artificial intelligence concepts.
Instructor: Dr. Tyler Banks - tylerbanks@ucmo.edu
Artificial Intelligence: A Modern Approach, 4th Edition by Peter Norvig and Stuart Russell
(CS 2400 Discrete Structures or Math 2410 Discrete Mathematics) and CS 2300 Data Structures or instructor consent
Upon completion of this course, students will be able to:
In addition, a student taking this course for graduate credits will be able to:
| Percent (%) | Grade |
|---|---|
| 90 - 100 | A |
| 80 - 89 | B |
| 70 - 79 | C |
| 60 - 69 | D |
| 0 - 59 | F |
Graduate-level Assessment: If a student intends to earn graduate credits in this course, he or she must make an appointment with the instructor at the beginning of the semester. Furthermore, these students will be required to complete an individual term project for an AI application; this additional project is designed to ensure compliance with the university policy regarding courses that can be taken for either graduate or undergraduate credits.
Midterm and Final Exams: The midterm and final exams will consist of questions that emphasize the principles and practical aspects for AI. In addition, the midterm and final exams will be divided into two sets of questions; one set for undergraduate students and the other set for graduate students. The set of questions for undergraduate students will focus on practical aspects while the set of questions for graduate students will emphasize on both practical and theoretical aspects for AI.
Assignments: There will be homework assignments, primarily from the textbook, and programming problems. Both undergraduate and graduate students will complete the same assignments. All assignments are due by midnight on the date listed. No late work will be accepted without an extenuating circumstance.
Final Project: The student will design his/her own AI solution to a practical application. As a result, the term project will strengthen students' critical thinking skills on the content of AI. A student who is taking this course for graduate credits will complete a term project individually at a higher standard. Undergraduates will complete a similar project as a group.
| Date | Topics/Slides | Readings | Deadlines |
|---|---|---|---|
| 1/14 (Week 1) |
Introduction | Russell & Norvig Ch. 1.1-5, 2.1-4 | - |
| 1/16 | Intro + Agents | Russell & Norvig Ch. 2.1-4 | - |
| 1/21 (Week 2) |
Uninformed Search - Reflex, DFS, BFS, UCS Trees | Russell & Norvig Ch. 3.1-4 | - |
| 1/23 | Informed Search - Greedy, Heuristics, A* | Russell & Norvig Ch. 3.5-6 | - |
| 1/28 (Week 3) |
Local Search - Graph Search, Hill Climbing | Russell & Norvig Ch. 4.1-2 | - |
| 1/30 | Games 1 - Minimax, Alpha-Beta Pruning | Russell & Norvig Ch. 5.1-4 | Homework 1 (Jan 31) |
| 2/4 (Week 4) |
Games 2 - Expectimax, Monte Carlo Trees | Russell & Norvig Ch. 5.5-7 | - |
| 2/6 | Constraint Satisfaction Problems (CSPs) 1 | Russell & Norvig Ch. 6.1-5 | - |
| 2/11 (Week 5) |
Constraint Satisfaction Problems (CSPs) 2 | Russell & Norvig Ch. 6.1-5 | - |
| 2/13 | Intro to Probability | Russell & Norvig Ch. 13.1 | Homework 2 (Feb 15) |
| 2/18 (Week 6) |
No class (snow) | Russell & Norvig Ch. 13.2 | - |
| 2/20 | Bayes Nets: Bayesian Networks | Russell & Norvig Ch. 13.2 | - |
| 2/25 (Week 7) |
Bayes Nets: Inference | Russell & Norvig Ch. 13.3 | - |
| 2/27 | Bayes Nets: Sampling | Russell & Norvig Ch. 13.4 | - |
| 3/4 (Week 8) |
Midterm Review | - | Homework 3 |
| 3/6 | MIDTERM | - | - |
| 3/11 | SPRING BREAK | SPRING BREAK | - |
| 3/13 | NO CLASS | NO CLASS | - |
| 3/18 (Week 9) |
Hidden Markov Models, Markov Chains | Russell & Norvig Ch. 14.1-5 | - |
| 3/20 | HMMs (Forward Algorithm, Viterbi Algorithm), Dynamic Bayes Nets, Particle Filtering | Russell & Norvig Ch. 16.1-3 | - |
| 3/25 (Week 10) |
Utility Theory, Rationality, Decision Networks, VPI | Russell & Norvig Ch. 16.1-3 | - |
| 3/27 | MDPs: States, Values, Policies, Q-values | Russell & Norvig Ch. 16.5-16.7 | - |
| 4/1 (Week 11) |
MDPs: Dynamic Programming | Russell & Norvig Ch. 17.2 | - |
| 4/3 | Machine Learning 1 - Naïve Bayes | Russell & Norvig Ch. 19.6 | Homework 4 (Apr 4) |
| 4/8 (Week 12) |
Machine Learning 2 - Perceptrons | Russell & Norvig Ch. 21.1-21.3 | - |
| 4/10 | Machine Learning 3 - Linear and Logistic Regression | Russell & Norvig Ch. 21.4-6 | - |
| 4/15 (Week 13) |
Machine Learning 4 - Optimization and Neural Nets | Russell & Norvig Ch. 19.9 | - |
| 4/17 | Natural Language Processing (NLP) | Russell & Norvig Ch. 23 | Homework 5 (Apr 18) |
| 4/22 (Week 14) |
Large Language Models (LLMs) | - | - |
| 4/24 | RL: Reinforcement Learning I | Russell & Norvig Ch. 22.1-22.6 | - |
| 4/29 (Week 15) |
RL: Reinforcement Learning II | Russell & Norvig Ch. 22.1-22.6 | - |
| 5/1 | Final Review Project Presentations |
- | Final Project (May 1) |
| 5/5 - 5/9 (Finals Week) |
FINALS | FINALS | - |
| Type/Number | Assignment | Due Date | Answers |
|---|---|---|---|
| Homework 1 | Agents and Search | 01/31/2025 | - |
| Homework 2 | Games | 02/14/2025 | - |
| Homework 3 | Probability and Bayes | 03/1/2025 | - |
| Homework 4 | Logic and Planning | 04/04/2025 | - |
| Homework 5 | Machine Learning | 04/18/2025 | - |
| Final Project | Final Project | 05/01/2025 | - |
1. Your UCM email account will be used, frequently, by the instructor, to communicate messages. It is your responsibility to check this account regularly.
2. Junior/Sensior students are held to a higher standard - late homework will not be accepted without an extenuating circumstance. Please plan ahead.
3. Assignments will be posted by email and/or Blackboard. It is the responsibility of the student to frequently check their email and Blackboard for course changes and updates.
4. The assigned textbook is not required but will serve as a good reference.
5. Students with documented disabilities who are seeking academic accommodations should contact the Office of Accessibility Services, Union 222, oas@ucmo.edu, 660.543.4421.
6. Advanced arrangements for unavoidable absences should be made whenever possible. Neither absence nor notification of absence relieves you of the responsibility of meeting all course requirements.
7. Homework is to be done independently unless otherwise directed.
8. ChatGPT/LLM use policy: You are allowed to consult with LLMs to answer specific questions about code, as an information provider. However, using GPT to generate entire portions of your homework assignment is not allowed, the work must be your own. Assignments suspected of doing so will receive a 0.
9. Any form of academic dishonesty will be dealt with according to the guidelines found at https://www.ucmo.edu/offices/general-counsel/university-policy-library/academic-policies/academic-honesty-policy/.
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