Links to classroom slides will appear below.
Lecture Notes are in Microsoft PowerPoint format. You can view them with either Microsoft PowerPoint or the free Microsoft PowerPoint Viewer.
Module 0: Introduction
AIMA Textbook: Part I
- Introduction
- Reading: AIMA, Chapter 1
- 9/3 [Slides: Introduction to Artificial Intelligence]
- 9/3 [Handout: Course Syllabus]
- Intelligent Agents
- Reading: AIMA, Chapter 2
- 9/5 [Slides: Intelligent Agents ]
- Python Programming
- Readings: Slides should be sufficient. See Resources for additional materials, including on-line Python library reference and on-line books.
- 9/8 [Slides: Introduction to Python I]
- 9/12 [Slides: Introduction to Python II]
- 9/15 [Slides: Introduction to Python III]
Module 1: Search
AIMA Textbook: Part II
- Search
- Reading: AIMA, Chapter 3.1-3.4, **3.5 on your own**, relevant part of 3.7
- 9/19
[Slides: Uninformed Search (final revision 9/25) ]
- Reading: AIMA, Chapter 4.1-4.3, relevant part of 4.6
- 9/26 [ Slides: Informed Search Part 1 ]
- 9/29 [Slides: Informed Search Part 2 ]
- 10/1 [Slides: Local Search ]
- Reading: AIMA, Chapter 6.1-6.4, 6.6 (Note that we're skipping Chap. 5 for right now)
- 10/3 [Slides:
2-Player Games: Adversarial Search Part 1 (unchanged from 2007)]
- 10/3 [Slides:
2-Player Games: Adversarial Search Part 2 (unchanged from 2007]
- Reading: AIMA, Chapter 24.4 subsection on Contour (pp 881-884)
- Reading: AIMA, Chapter 5.1-5.3
- 10/6 [Slides: Interpreting Line Drawings ]
- 10/6 [Handout:
The Huffman-Clowes Labelling Set ]
- 10/8 [Slides: Interpreting Line Drawings: Constraint Satisfaction & AC-3 ]
- 10/10 [Slides: An Overview of Constraint Satisfaction ]
Midterm I - Monday, October 20
10/17 [Review Slides for Midterm ]
Module 2: Machine Learning and Natural Language Processing
- Introduction to Probability
- Readings: AIMA, Chapter 13
- 10/22 [ Slides Uncertainty
& Probability (revised) ]
- Naive Bayes & Spam Filtering
- Readings: AIMA, Chapter 20.1
- 10/27 [Slides: Naive Bayes/Spam Filtering]
- Bayesian Networks
- Readings: AIMA, Chapter 14.1-14.2
- 10/31 [Slides:Bayes Nets]
- Perceptrons and Support Vector Machines
- Introduction to Natural Language Processing
- Readings: None, other than slides
- 11/05 [Slides: Intro to NLP]
- Hidden Markov Models & Part of Speech Tagging
- 11/10 Readings: AIMA, CHapter 15.1-15.3
- [ Slides: Intro to HMMs and Part of Speech tagging]
Module 3: Knowledge Representation and Logic
AIMA Textbook: Part III
- Logical Agents & Propositional Logic
- Reading: AIMA, Chapter 7.1-7.4
- 11/17 [Slides: Logical Agents ]
- 11/19 [
Slides: Proof Methods for Propositional Logic ]
- First Order Logic
- Readings: AIMA, Chapter 8, Chapter 9.1-9.2, Chapter 9.5 (through p. 300)
- 12/01 [ Slides: First Order Logic ]
- 12/03 [ Slides: Inference in FOL ]
Midterm II
Time: Wednesday, December 10, 12-2p.m.
- 11/03 [Slides: Review for Midterm II ]
- [Practice Midterm II (from 2007) ]
- [Propositional Logic Practice Problem]
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HOMEWORK ASSIGNMENTS
- Homework 1 (in .PDF format) - Due Fri., 26 Sept.
You can find the encrypted file vfd.txt for the homework here. (Right click and then use "Save Link As..." or "Save Target As.." to save it...) - Homework 2 (in .PDF format) - Everything except Part II, #4 (the
programming) due Wednesday, 8 October. Part II, #4 due Friday 17 October.
Python utility files for #4 of part 2: robotUtilities.py, guiHelp.py. Test files: square_in_the_middle, triangle_in_the_middle, tall_triangle_in_the_middle, big_triangles. - Homework
3 (in .PDF format) - Problems 1 and 2 due Friday, Oct 31. Problems 3-6 due Wed, Nov 5
- Examples to test on:
- ac3solvable_example,
- dp_puzzle,
- gentle_sudoku,
- moderate_sudoku,
- tough_sudoku,
- diabolical_sudoku
- guessing_puzzle
FINAL PROJECT
Auxiliary files including training data (in .zip format): tichu.zip
Auxiliary files (in .zip format): tichuFinal.zip,
Some potentially useful resources on Tichu:
- Wikipedia article on Tichu
- Links from the Wikipedia page immediately above
- An editted set of Tichu rules
- Tichu Tips and Tricks
- Tichu Strategy
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OTHER RESOURCES
Python Resources
- http://www.python.org/
and especially- the Library Reference at http://docs.python.org/tut/tut.html
- and the tutorial at http://docs.python.org/tut/tut.html
- http://www.rexx.com/~dkuhlman/python_101/python_101.html
- http://www.rexx.com/~dkuhlman/python_201/python_201.html
- http://www.awaretek.com/tutorials.html
- Downloadable versions of the book "Dive into Python"
- You can access O'Reily's Safari website, which has books such as Learning Python, Programming Python, Python in a Nutshell, and Python Cookbook, from the Penn library e-resources page. (Type "python" in the search box when you finally get to the Safari page.)
Textbook Resources
Website for: Artificial
Intelligence: A Modern Approach
(http://aima.cs.berkeley.edu/)
For more information, please contact mitch (AT) cis.upenn.edu