CIS 391 - Artificial Intelligence
Fall 2014


COURSE STRUCTURE
* MODULES AND NOTES * ASSIGNMENTS * RESOURCES

Instructors
Mitch Marcus
Levine 503, 215-898-2538
mitch (AT) standard local email address
Office Hours: TBA & and by appointment
Teaching Assistants
Mitchell Stern
mitstern (AT) standard local email address
Office Hours: TBA
Office Hours Location: TBA
Jennifer Hui
jenhui(AT) standard local email address
Office Hours: TBA
Office Hours Location: TBA
Tim Kim
timkim (AT) standard local email address
Office Hours: TBA
Office Hours Location: TBA
Daniel Moroz
dmoroz (AT) standard local email address
Office Hours: TBA
Office Hours Location: TBA
Course Administrator
Cheryl Hickey
Levine 502, 215-898-3538
cherylh (AT) standard local email address

Class Schedule: Tuesday & Thursday noon-1:30pm Wu & Chen Auditorium

Artificial Intelligence is considered from the point of view of a resource-limited knowledge-based agent who must reason and act in the world. Topics include search, knowledge representation and reasoning, probabilistic reasoning, machine learning, logic, automatic theorem proving, and natural language processing. Programming assignments in Python.

Prerequisites: CIS 120, 121. Introductory statistics, introductory logic, and familiarity with finite state automata are all useful.

COURSE STRUCTURE


Web Page:
http://www.seas.upenn.edu/~cis391/

Textbook:
Artificial Intelligence: A Modern Approach
(Third Edition) 2009
Stuart Russell and Peter Norvig
Prentice Hall Series in Artificial Intelligence
Only the Third Edition, (Not International) will match the homeworks
Grading:
50% Homeworks
25% Midterm 1
25% Midterm 2
Homework:
Homework will be due at 11:59 on specified dates with submission cut off promptly. Late homework will not be accepted unless an extension has been granted in advance. Your lowest homework grade will be dropped, so extensions will be granted very sparingly.

We expect homeworks to be more frequent than in previous versions of this course, with about 50% more homeworks this year.

Please note that we insist that students follow Penn's Code of Academic Integrity, and that academic dishonesty, as defined in the Code of Integrity will not be tolerated.


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CLASS MODULES


Links to classroom slides will appear below.

Lecture Notes are in PDF format.

Module 0: Introduction  (AIMA Textbook: Chapters 1-2)

Module 1: Search (AIMA Textbook: Chapters 3-6)

Module 2: Machine Learning and Natural Language Processing

Module 3: Knowledge Representation and Logic


MIDTERM 2 - Monday, December 15, 12 p.m. Place: TBD


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HOMEWORK ASSIGNMENTS

PDF Files require Adobe Acrobat Reader
DOC Files require Microsoft Word or Open Office