CS 2710 / ISSP 2610, Foundations of Artificial Intelligence, Fall 2015

Syllabus

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When and Where: Fall 2015 T Th 1:00pm-2:15pm, 5313 Sennott Square
Professor:  Dr. Jan Wiebe, Sennott Square Rm 5409, 412-624-9590, wiebe@cs.pitt.edu, http://www.cs.pitt.edu/~wiebe
Professor Office Hours: Thurs 2:15-4:15pm and by appointment
Teaching Assistant (TA): Luca Lugini, Sennott Square Rm 6150, https://people.cs.pitt.edu/~lucalugini/courses/fall2015/CS2710/
TA Office Hours: Wed 10am-12pm and by appointment
Prerequisites:  Knowledge of Computer Science through CS1501 and CS1502.
Textbook:  Artificial Intelligence: a modern approach (third edition), S. Russell and P. Norvig, Prentice Hall. Be sure to get the third edition (the one with the blue cover).
Grade Basis:

Assignments: Assignments will be mixtures of programming and written assignments. Some involve extending code given to you. The code will be in Python. If you have never used Python, go through the tutorial on the Python website.

Due dates are strict. Extensions will be granted only in the case of (documented) extreme circumstances.
Exams: Exams will be closed book/closed notes/closed electronics.

The final exam will cover the entire course.

The "wrap up" slides at the end of lecture notes give guidance about what you are responsible for.

Generally speaking, you should be able to apply the algorithms, heuristics, and techniques to examples without having the code, lecture notes, or the book in front of you.

When you are asked to trace an algorithm, I will ask a specific question, such is what is currently on the fringe as a search progresses.

Questions about knowledge representation schemes or writing logical sentences will be closely related to examples we cover in class or on the assignments.

Other types of questions include definitions; the differences/relations among algorithms or techniques; complexity results; give an example of something; specify the components of a data structure; and other such specific questions.
Addressing a question from lecture: You can offer to answer/address a question/issue that comes in class that is In up to two pages plus references, clearly state the question/issue you are addressing, and write up a response. Email your response to me. Acceptable answers plus comments will be posted for other students to read (you can decide if you want to include your name). For an unacceptable response, I'll send comments to the author about why I find it unacceptable. There is no penalty for an unacceptable response.
Course Schedule: There is a schedule on the course web page. The schedule includes topics, lecture notes, due dates, assignments, specific reading assignments, and exam dates. It will be updated as the course proceeds.
Academic Misconduct: Assignments must be your own individual work.

You must do the work without undue help from other people, and you must not present material from resources such as the Web, books, papers, previous classes, or other people as your own. You may talk to each other about concepts and techniques, but you must not discuss specific solutions or approaches to solutions.

Academic Integrity Code for the Deitrich School of Arts & Sciences
Students With Disabilities: If you have a disability for which you are or may be requesting an accommodation, you are encouraged to contact both your instructor and Disability Resources and Services, 216 William Pitt Union, (412) 648-7890/(412) 383-7355 (TTY), as early as possible in the term. DRS will verify your disability and determine reasonable accomodations for this course.
Planned Course Content:
Part 0 (1 lecture): Introduction. [Chapters 1 and 2]
Part I (approximately 7 lectures): State-Space Search; Heuristic Search; Constraint Satisfaction Problems; Adversarial Search. [Chapters 3,4,5,6]
Part II (approximately 5 lectures): Knowledge Representation, Reasoning, Logic [Chapters 7,8,9,12]
Part III (approximately 5 lectures): Planning [Chapter 10]
Part IV (approximately 8 lectures): Reasoning under Uncertainty; Learning [Chapters 13,14,17,18]
Acknowledgements: lectures slides include material from lecture notes by Yu Cao , Claire Cardie, Hal Daume, Charles Elkan, Reva Freedman, Shaun Gause, Reijer Grimbergen, Milos Hauskrecht, James Martin, Hwee Tou Ng, Peter Norvig, Ellen Riloff, Stuart Russell, Ellen Walker.