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.

• Introduction
  • 8/27   Introduction to Artificial Intelligence [(pdf) ][(6-up) ] (AIMA, Chapters 1,2)
• Python Programming
  • Readings: Slides should be sufficient. See Resources for additional materials, including on-line Python library reference and on-line books.
  • 9/1  Python Overview 1 [(pdf) ] [(6up)  ]
  • 9/3  Python Overview 2 [(pdf) ] [(6up)  ]
      Thorough Python tutorial [(pdf)][(6up)]

• Uninformed Search
  • 9/8 Search Problem Formulation  [(pdf) ] [(6up) ] (AIMA 3.1-3.3)
  • 9/10 Uninformed Search   [(pdf) ] [(6up) ] (AIMA 3.4)
  • 9/15 NO CLASS
• Informed Search
  • 9/17  Informed Search Part I [(pdf) ] [(6up) ] (AIMA 3.5.1-3.5.2)
  • 9/22   Informed Search Part II: Hill climbing, simulated annealing, genetic algorithms [(pdf)] [(6up)] [(pptx)] (AIMA 3.6, 4.1)
• Adversarial Search
  • 9/24 & 9/29  2-Player Games: Adversarial Search [(REVISED pdf)][(REVISED 6up)] (AIMA 5.1-5.2)
• Constraint Satisfaction
  • 10/1 & 10/6  Introduction to Constraint Satisfaction [(pdf)] [(6-up)] (AIMA 6.1-6.4)
  • 10/8   FALL BREAK
• A practice exam with solutions is available here
  This exam may vary in format from your exam.

• Introduction to Probability
  • 10/13   Uncertainty & Probability [(pdf)] [(6-up)] (AIMA 13.1-13.5)
    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)][(6-up)] (AIMA 22.2)
  • 10/20  MIDTERM 1
  • 10/22   Naive Bayes & Spam Filtering [(pdf)][(6-up)] AIMA 22.2)
• Graphical Models: Bayesian Networks & LDA
  • 10/27   Bayes Nets & LDA [(pdf) ] [(6-up) ] (AIMA 14.1-14.3)
• Topics in Natural Language Processing and Human Language Technology
  • 10/29   Intro to NLP [(pdf)] [(6-up)]
  • Hidden Markov Models for Tagging and Speech Recognition
    • 11/3   Introduction to Markov Models (revised) [(pdf)][(6-up)]
      (some topics covered in AIMA 15.2;
      Markov Models in Jurafsky & Martin, Speech and Language Processing, 4.1-4.3;
      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)][(6-up)]
    • 11/10   Hidden Markov Models - Three Formal Problems [(pdf)][(6-up)]
      (some topics covered in AIMA 15.3. Good discussion in J&M, 5.1, 5.2, 5.5)
      
    • 11/12   Speech Recognition [(pdf)][(6-up)] (parallel treatment in AIMA 23.5)
  • Perceptrons and Support Vector Machines
  • 11/17   Perceptrons and Support Vector Machines [(pdf)][(6-up)]
    (parallel to some topics covered in AIMA 18.1-2, 18.6.3, 18.9 Good discussion in J&M, 5.1, 5.2, 5.5)
    

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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