CIS 391  Artificial Intelligence
Fall 2015
LECTURE SCHEDULE AND HANDOUTS * ASSIGNMENTS * RESOURCES
Instructors 

Mitch Marcus
Levine 503, 2158982538
mitch (AT) standard local email address
Office Hours: See Piazza for regular schedule & by appointment

Teaching Assistants 

Daniel Moroz
dmoroz (AT) standard local email address
Office Hours: See Piazza for schedule

Toma Pigli
tpigli (AT) standard local email address
Office Hours: See Piazza for schedule

Course Administrator 

Cheryl Hickey
Levine 502, 2158983538
cherylh (AT) standard local email address

Class Schedule: Tuesday & Thursday noon1:30 Wu & Chen Auditorium (Levine 101)
Artificial Intelligence is considered from the point of view of a
resourcelimited knowledgebased 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.
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. You can submit up to two homeworks late, but extensions after that will be granted
only for true emergencies. Your lowest homework grade will be dropped.
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, and penalties will be severe.
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Links to classroom slides will appear below.
Lecture Notes are in PDF format.
Module 0: Introduction (AIMA Textbook: Chapters 12)
 Introduction
 Python Programming
 Readings: Slides should be sufficient. See Resources for
additional materials, including online Python library reference and online books.
 9/1 Python Overview 1
[(pdf) ]
[(6up) ]
 9/3 Python Overview 2
[(pdf) ]
[(6up) ]
Thorough Python tutorial
[(pdf)][(6up)]
Module 1: Search (AIMA Textbook: Chapters 36)
 Uninformed Search
 9/8 Search Problem Formulation [(pdf) ]
[(6up) ]
(AIMA 3.13.3)
 9/10 Uninformed Search [(pdf) ]
[(6up) ] (AIMA 3.4)
 9/15 NO CLASS
 Informed Search
 9/17 Informed Search Part I [(pdf) ]
[(6up) ] (AIMA 3.5.13.5.2)
 9/22 Informed Search Part II: Hill climbing, simulated annealing, genetic algorithms
[(pdf)]
[(6up)] [(pptx)] (AIMA 3.6, 4.1)
 Adversarial Search
 Constraint Satisfaction
 10/1 & 10/6 Introduction to Constraint Satisfaction [(pdf)]
[(6up)] (AIMA 6.16.4)
 10/8 FALL BREAK
 A practice exam with solutions is available here
This exam may vary in format from your exam.
Module 2: Machine Learning and Natural Language Processing
 Introduction to Probability
 10/13 Uncertainty & Probability [(pdf)]
[(6up)] (AIMA 13.113.5)
A review of discrete probabiliity theory, excerpted from Prof. Jean Gallier's discrete
mathematics textbook, can be found [here.]
 Conditional Probabilities, Naive Bayes & Spam Filtering
 10/15 Bayes Rule & Naive Bayes [(pdf)][(6up)] (AIMA 22.2)
 10/22 Naive Bayes & Spam Filtering [(pdf)][(6up)] AIMA 22.2)
 Graphical Models: Bayesian Networks & LDA
 10/27 Bayes Nets & LDA [(pdf) ]
[(6up) ] (AIMA 14.114.3)
 Topics in Natural Language Processing and Human Language Technology
 10/29 Intro to NLP [(pdf)]
[(6up)]
 Hidden Markov Models for Tagging and Speech Recognition
 11/3 Introduction to Markov Models (revised)
[(pdf)][(6up)]
(some topics covered in AIMA 15.2;
Markov Models in
Jurafsky & Martin, Speech and Language Processing,
4.14.3;
Smoothing in J & M Chap. 4.5 Intro and 4.5.1)
 11/5 Part of Speech Tagging and Hidden Markov Models  Part 1
[(pdf)][(6up)]
 11/10 Hidden Markov Models  Three Formal Problems
[(pdf)][(6up)]
(some topics covered in AIMA 15.3. Good discussion in J&M, 5.1, 5.2, 5.5)
 11/12 Speech Recognition
[(pdf)][(6up)]
(parallel treatment in AIMA 23.5)
 Perceptrons and Support Vector Machines
 11/17 Perceptrons and Support Vector Machines
[(pdf)][(6up)]
(parallel to some topics covered in AIMA 18.12, 18.6.3, 18.9 Good discussion in J&M, 5.1, 5.2, 5.5)
MiniModule 3: Knowledge Representation and Logic
 Propositional Logic & Inference
 11/19 Logical Agents, Introduction to Logic & Propositional Logic
[(pdf)][(6up)] (AIMA 7.17.4)
 11/24 & 12/1 Propositional Inference
[(pdf)][(6up)] (AIMA 7.17.4)
 11/26 Thanksgiving
 Final Lecture
UPDATE: MIDTERM 2 WILL BE HELD DURING THE LAST CLASS, TUESDAY, DECEMBER 8
A practice midterm with solutions is available here.
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PDF Files require Adobe
Acrobat Reader DOC Files require Microsoft Word or Open
Office
 Homework 1
Due by 11:59 pm on Thursday, September 10, 2015.
Additional files: skeleton file.
 Homework 2
Due by 11:59 pm on Tuesday, September 22, 2015.
Additional files: skeleton file,
Lights Out GUI.
 Homework 3
Due by 11:59 pm on Tuesday, Oct 6, 2015.
Additional files: skeleton file,
Tile Puzzle GUI,
Grid Navigation GUI,
simple scene,
barrier scene,
random 50x50 scene,
Dominoes Game GUI.
 Homework 4
Due by 11:59 pm on Thursday, October 15, 2015.
Additional files: skeleton file,
test puzzles,
test puzzle solutions.
 Homework 5
Due by 11:59 pm on Thursday, October 29, 2015.
Additional files: skeleton file,
training and development data.
 Homework 6
Due by 11:59 pm on Thursday, November 5, 2015.
Additional files: skeleton file,
training and development data (same as previous homework).
 Homework 7
Due by 11:59 pm on Thursday, November 12, 2015
Additional files: skeleton file,
Frankenstein.
 Homework 8
Due by 11:59 pm on Thursday, November 19, 2015
Additional files: skeleton file,
Brown corpus.
 Homework 9
Due by 11:59 pm on Tuesday, December 1, 2015
Additional files: skeleton file,
data sets.
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Python Resources

Textbook Resources
Website for: Artificial
Intelligence: A Modern Approach
(http://aima.cs.berkeley.edu/)
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For more information, please contact mitch (AT)
cis.upenn.edu