Course Description
Fundamental issues in intelligent systems. Knowledge representation and mechanisms of reasoning. Search and constraint satisfaction. Agents. Application areas of AI and current topics. Students will learn basic concepts of artificial intelligence and get some hands on experience to solve real-life problems. Python will be used for all the assignments.
Textbook
B1. Artificial Intelligence: A Modern Approach
B2. Convex Optimization (optional)
B3. Applied Mathematical Programming (optional)
Communication
All written communication should be directed though Google Classroom. You can post publicly or privately depending on your preference. Emails won’t be responded to.
Schedule
| Date | Lecture | Misc |
|---|---|---|
| Problem Solving and Search | ||
| Tue Jan 08 | Introduction To AI | Ch 1 and Ch 2 |
| Thu Jan 10 | Uninformed Search | Ch 3.1-3.4 |
| Tue Jan 15 | Informed and Local Search | Ch 3.5-3.7, 4.1-4.2 |
| Thu Jan 17 | Constraint Satisfaction | Ch 6 |
| Multi-Agent Systems | ||
| Tue Jan 22 | Adversarial Search | Ch. 5.1-5.3 |
| Thu Jan 24 | Game Trees | Ch. 5.4-5.5, 16.1-16.3 |
| Tue Jan 29 | Game Theory | Ch 17.5 |
| Thu Jan 31 | Social Choice Mechanism Design | Ch 17.6 |
| Optimization | ||
| Tue Feb 05 | Convex Optimization | Ch 1 and 4 of B2 |
| Thu Feb 07 | Linear Program | Ch 2 and 4 of B3 |
| Tue Feb 12 | Mixed Integer Linear Programming | Ch 9 of B3 |
| Sequential Decision Making | ||
| Thu Feb 14 | Markov Decision Processes - I | Ch 17.1-17.3 |
| Tue Feb 19 | Markov Decision Processes - II | Ch 17.1-17.3 |
| Thu Feb 21 | Reinforcement Learning - I | Ch 21 |
| Tue Feb 26 | Reinforcement Learning - II | Ch 21 |
| Thu Feb 28 | Midterm Exam | In Class |
| Tue Mar 05 | Spring Break; No Class | |
| Thu Mar 07 | Spring Break; No Class | |
| Probabilistic Reasoning | ||
| Tue Mar 12 | Probability Model | Ch 13.1-13.5 |
| Thu Mar 14 | Bayesian Networks: Representation | Ch 14.1-14.2, 14.4 |
| Tue Mar 19 | Bayesian Networks: Independence | Ch 14.3 |
| Thu Mar 21 | Bayesian Networks: Inference | Ch 14.4 |
| Tue Mar 26 | Bayesian Networks: Sampling | Ch 14.4-14.5 |
| Thu Mar 28 | Decision Networks | Ch 16.5-16.6 |
| Tue Apr 02 | Hidden Markov Models | Ch 15.2, 15.5 |
| Thu Apr 04 | Particle Filtering | Ch 15.2, 15.6 |
| Machine Learning | ||
| Tue Apr 09 | Naive Bayes | Ch 20.1-20.2 |
| Thu Apr 11 | Perceptrons and Logistic Regression | Ch 18.6.3 |
| Tue Apr 16 | Neural Networks | Ch 18.8 |
| Thu Apr 18 | Decision Trees | - |
| Tue Apr 23 | Applications: Perception, Robotics, Language | - |
| Thu Apr 25 | Final Review | |
| Wed May 01 | Final Exam (10:00am-12:00pm) |