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Instructor Location and Time Office Hours |
Amarda Shehu , Room #4422 ENG, amarda\AT\gmu.edu Innovation Hall #204, W 4:30pm - 7:10 pm W 2:30-4:30 pm |
This is a foundational course in Artificial Intelligence (AI), which is the discipline of designing intelligent computer systems. The undercurrent of the topics covered in this course is how to design and analyze autonomous agents that process information, plan, and execute actions in the presence of constraints, limited information, and limited computational resources. Central questions will be addressed:
The set of topics covered in this course will closely follow the chapters in the Russell & Norvig textbook. They include uninformed, informed, heuristic, and adversarial search, constraint satisfaction (chapters 3-6), knowledge representation and first-order logic (chapters 7-9), planning and knowledge representation(chapters 10-12), reasoning in the presence of uncertainty (chapters 13-17), learning (chapters 18-21), and natural language processing (chapters 22-25). Not all topics will be covered at the same level of detail and depth. An attempt will be made to study topics in the context of interesting, current examples from various sub-domains of modeling of intelligent agents, such as self-driving cars, face and handwriting recognizers, game playing programs, package delivery robots, schedulers, spam detectors, named-entity recognizers, and speech recognition systems.
The course draws on material from computer science, probability theory, decision theory, and game theory. Good knowledge of algorithms, discrete mathematics, linear algebra, probability and statistics is assumed. Usage of functional languages, such as LISP and Prolog is encouraged. Matlab, expert system programming languages, and other languages may be employed on relevant topics during the course. Students have some flexibility in the programming language they select for homeworks upon prior approval from the instructor.
Material will be disseminated in the form of lectures. Students are advised to read the corresponding material in the textbook before class. Additional reading material may be disseminated on specific topics to provide more depth. Students will be tested on the comprehension of the basic material through homeworks and exams. Homeworks may include programming. No programming is involved in the exam, only pseudocode. No late homeworks or project deliverables will be accepted.
CS 310 and CS 330.
| Date | Topic | Lectures | Assignments |
|---|---|---|---|
| Jan. 20 | What is AI? | Chapters 1-2 |
| Problem Solving |
| Jan. 27 | Uninformed vs. Informed Search | Chapter 3 | Hw1 Out | ||||||||||||||||||||||||||||||||||||
| Feb. 03 | Heuristic Search, Adversarial Search | Chapters 4-5 | |||||||||||||||||||||||||||||||||||||
| Feb. 10 | Constraint Satisfaction | Chapter 6 | Hw1 Due, Hw2 Out | ||||||||||||||||||||||||||||||||||||
| Knowledge and Reasoning | ||
| Feb. 17 | Logical Agents | Chapter 7 | Hw2 Due | ||||||||||||||||||||||||||||||||||||
| Feb. 24 | First-order Logic | Chapter 8 | |||||||||||||||||||||||||||||||||||||
| Mar. 02 | Inference in First-order Logic | Chapter 9 | |||||||||||||||||||||||||||||||||||||
| Mar. 09 | Spring Break | ||||||||||||||||||||||||||||||||||||||
| Mar. 16 | Classical Planning | Chapter 10 | short Exam | ||||||||||||||||||||||||||||||||||||
| Mar. 23 | Planning and Knowledge Representation | Chapters 11-12 | Hw3 Out | ||||||||||||||||||||||||||||||||||||
| Uncertainty and Probabilistic Reasoning | ||
| Mar. 30 | Probabilistic Reasoning and Bayesian Networks | Chapters 13-14 | Team Report |
| Apr. 06 | Inference in Bayesian Networks | Chapter 14 | White paper report |
| Apr. 13 | Temporal Probability Models | Chapter 15 | Hw3 Due |
| Learning | ||
| Apr. 20 | Representation of Uncertainty, Bayesian Methods | Chapters 16-20 | |
| Apr. 27 | NLP, Perception, Robotics | Chapters 22-25 | Preliminary Project Report |
| May 04 | Final Exam | TBA | TBA (submit project via blackboard) |
The class enforces the GMU Honor Code. Violations of academic honesty will not be tolerated.
If a disability or other condition affects your academic performance, document it with the Office of Disability Services.
Latest lectures, schedule updates, and other course materials will be
available at URL
http://www.cs.gmu.edu/~ashehu/?q=CS580_Spring2016