CIS 391  Artificial
Intelligence
Fall 2013
COURSE STRUCTURE * MODULES AND NOTES * ASSIGNMENTS * RESOURCES
Instructor
Mitch Marcus
Levine 503, 2158982538
mitch (AT) cis.upen.edu (debug...)
Office Hours: Tuesday, 4:155:45 PM & by appointment (email).
Course Administrator
Cheryl Hickey
Levine 502, 2158983538
cherylh (AT) cis.upen.edu 
Teaching Assistants
Trisha Kothari
Location Levine 612
kotharit (AT) standard local email address
Office Hours: Tuesday, 78:30 PM
Menghan Li
Location Levine 512
menghanl (AT) standard local email address
Office Hours: Wednesday, 89:30 PM

Class
Schedule:Tuesday & Thursday noon1:30 PM,
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 121
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:
 40% Homeworks (Final project will count for 1/4 of that amount)
 30% Midterm 1
 30% Midterm 2
 Homework:
 Homework will be due at noon on specified dates, unless otherwise specified
 Late homeworks will be penalized unless an extension has
been granted in advance :
  15% reduction if 024 hours late
  30% reduction if handed in at the following class
  45% reduction if handed in two classes later
  no credit if more than a week late
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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 12
 Introduction
 8/29 Introduction to Artificial Intelligence
[(pdf) ][(6up) ] ( AIMA, Chapter 2)
 9/3 Intelligent Agents [(pdf) ][(6up) ] ( AIMA, Chapter 2)
 9/5 NO CLASS
 Python Programming
 Readings: Slides should be sufficient. See Resources for additional materials, including online Python library reference and online books.
 9/10 Python Review
[(pdf) ][(6up) ]
Much more thorough Python tutorial
[(pdf)][(2up)]
Module 1: Search
AIMA Textbook: Chapters 36
 Uninformed Search
 9/12 Uninformed Search Part I [(pdf) ]
[(6up) ]
(AIMA 3.23.3)
 9/17 Uninformed Search Part II [(pdf) ]
[(6up) ](AIMA 3.4)
 Informed Search
 9/19 Informed Search Part I [(pdf) ]
[(6up) ] (AIMA 3.5.13.5.2)
 9/24 Informed Search Part 1I
[(pdf)]
[(6up)] (AIMA 3.6, 4.1)
 Adversarial Search
 9/26 2Player Games: Adversarial Search [(pdf)][(6up)] (AIMA 5.15.4)
 Constraint Satisfaction
 10/1 Interpreting Line Drawings via Constraint satisfaction
[(pdf)]
[(6up)](AIMA 6.1(roughly), 6.2)
 10/1 Handout: The HuffmanClowes Labelling Set [(pdf)]
 10/3 & 10/8 Introduction to Constraint Satisfaction [(pdf)]
[(6up)] (AIMA 6.16.4)
**NEW**: **MIDTERM 1: TUESDAY, October 29**
 10/24 Review slides [(pdf)][(6up)]
 A practice midterm with solutions is available here
Module 2: Machine Learning and Natural Language Processing
 Introduction to Probability
 10/15 Uncertainty & Probability [(pdf)][(6up)] (AIMA 13.113.5)
 Naive Bayes & Spam Filtering
 10/17 Naive Bayes/Spam Filtering [(pdf)][(6up)] (AIMA 22.2)
 Graphical Models: Bayesian Networks & LDA
 10/22 Bayes Nets & LDA [(pdf) ]
[(6up) ] (AIMA 14.114.3)
 10/29 Midterm 1
 Topics in Natural Language Processing and Human Language Technology
 10/31 Intro to NLP [(pdf)]
[(6up)]
 Markov Models for Language Modeling, Hidden Markov Models for Tagging and Speech Recognition
 11/5 Language models and Markov Models
[(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)
 11/7 Part of Speech Tagging and Hidden Markov Models
[(pdf)][(6up)]
(some topics covered in AIMA 15.3. Good discussion in J&M, 5.1, 5.2, 5.5)
 11/14 Speech Recognition
[(pdf)][(6up)]
(parallel treatment in AIMA 23.5)
 11/21 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)
Module 3: Knowledge Representation and Logic
 11/2612/2 Propositional Logic & Inference
[(pdf)][(6up)] (AIMA 7.17.4)
 12/5 GUEST LECTURE  Prof. Chris CallisonBurch: Statistical Machine Translation
[(pdf)]
 12/10 An Intro to First Order Logic & Inference[ (pdf) ][ (6up) ] [][](AIMA 8.18.3, 9.19.2)
MIDTERM 2  Wednesday, December 18, 12p.m.2 Place: LRSM AUD, LRSM 112B
 A practice midterm with solutions is available here.
 Slides from midterm review session: [(pdf)][(6up)]
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HOMEWORK ASSIGNMENTS
PDF Files require Adobe
Acrobat Reader DOC Files require Microsoft Word or Open
Office
Homework submission instructions can be found here
 Homework 1
 **MODIFIED**: Due Thursday, September 26, 2013 by noon. Homework submission instructions can
be found here .
You can find the encrypted file vfd.txt for
Problem 8 here. (Right click and
then use "Save Link As..." or "Save Target As.." to save it...)
 Homework 2 
Due Thursday, October 10, at noon.
The code for the last question can be
found here.
 Homework 3  Due Thursday, October 31, at noon.
The sudoku problems to test on can be found here.
 Homework 4  Due Thursday, November 21, at noon..
A tokenized version of Jane Austen's Pride and Prejudice can be found here.
The Project Gutenberg original text can be found here.
 Homework 5  Due Thursday, December 5, at noon.
The code for this problem can be found here and the dataset can be found here.
 FINAL VERSION OF HOMEWORK 6: Homework 6  Due Thursday, December 19, 11:59 p.m.. This version includes detailed instructions on using the new autograding code framework.
The dataset can be found here.
The new code framework, allowing for autograding, can be found here.
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OTHER RESOURCES
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