Artificial Intelligence, Winter, 2010-2011


Course Number: 4003-455
Time: MW / 12-1:50 PM
Room:70-3560
Website: people.rit.edu/~jcdicsa/courses/AI/
Instructor: Justin Domke

Course Text: Artificial Intelligence: A Modern Approach, 3rd Ed., by Russell and Norvig.

Grading Scheme:

  • 20% Problem Sets (≈ 4)
  • 20% Programming Projects (2)
  • 25% Midterm
  • 35% Final

errata
Hw 1, Problem 3, should read: Lugoj to Bucharest
HW 3, Problem 2, The second constraint should read |r_i-r_j| != |i-j|, rather than |r_i-r_j| != |r_i-r_j| (which is impossible).

Projects:

Lectures:
DateTopicReadingNotes
Mon, Nov. 29Introduction and History 1.1, 1.3, 1.4, 1.5. Optional: Turing's 1950 paper
Wed, Nov. 30Classical Search (BFS, DFS, DLS, Iterative Deepening)3.1, 3.2, 3.3, 3.4HW1 Assigned
Mon, Dec. 6More Classical Search (A*, heuristic functions) Start Beyond Classical Search.3.5, 3.6
Wed, Dec. 8Python Tutorial (Guest Lecture by Haitao Du)Example Code
Mon, Dec. 13Local Search, Hill Climbing, Simulated Annealing, Local Beam Search4.1, 4.1.1, 4.1.2, 4.1.3
Wed, Dec. 15Genetic Algorithms, Start minimax, Local Search in Continuous Spaces4.1.4, 5.1, 5.2, 4.2HW1 Due, Proj 1 Assigned soon
Break
Mon, Jan. 3
Wed, Jan. 5Finish Local Search, Finish minimax & alpha-beta pruning
Mon, Jan. 10Constraint SatisfactionChapter 65.4.1, 5.4.2
Wed, Jan. 12More Constraint Satisfaction, Review for MidtermHW 2 due
Mon, Jan. 17
Midterm
Wed, Jan. 19Review midterms. More constraint satisfaction.Proj. 1 due
Mon, Jan. 24Finish constraint satisfaction. Uncertainty13.2, 13.2.1, 13.2.2, 13.2.3
Wed, Jan. 26Start Bayes NetsChapter 14
Mon, Jan. 31More Bayes Nets
Wed, Feb. 2Start HMMsHW 3 Due
Mon, Feb. 7Mostly finish HMMs, Start Learning
Wed, Feb. 9Two last HMM examples, Learning intro, Start decision trees.Ch. 18
Mon, Feb. 14Project 2 Due
Wed, Feb. 17
Fri, Feb. 25Final Exam from 10:15am-12:15pm

TENTATIVE Schedule:
Topic # LecturesBook sections
Introduction, History1Chap. 1
Classical Search2Chap. 3
Python Tutorial1
Beyond Classical Search1.5Chap. 4
Adversarial Search2Chap. 5
Prositional Logic1.5Chap. 7
First-Order Logic1Chap. 8
MIDTERM1
Uncertainty1Chap. 13
Bayesian Networks1.5Chap. 14
Hidden Markov Models1.5Chap. 15
Learning Intro, Cross Validation 1.518.1, 18.4
Decision Trees, Evaluating Hypotheses218.3, 18.4
Linear Methods & Perceptrons118.6
Neural Networks and Backpropagation1.518.7

Final Exam: Friday Feb. 25, 2011, 10:15am - 12:15pm (Location TBD)