Week #

Monday 
Wednesday 
Friday 
1 
Jan
5: Lecture (in MGH 231). Course introduction, definition of intelligence, Turing Test. Assignment 1 (Symbol manipulation in Python) given out.

Jan
7: Lecture (in MGH 231). Python functions; recursive functions on lists; lambda expressions; mapping; string operations. Example: Sequence prediction.

Jan
9: Lab (in MGH 044). Introduction to Python; interaction; function definition and calling; lists;

2

Jan
12: Lecture (in MGH 231). StateSpace Search: Introduction

Jan
14: Lecture (in MGH 231). StateSpace Search: Alphabeta search with TicTacToe. Checkers and Chess.

Jan
16: Lab (in MGH 044). Game playing. Assignment 1 due. Assignment 2 (Statespace search) given out.

3

Jan
19: Martin Luther King Holiday (no class) 
Jan
21: Lecture (in MGH 231). StateSpace Search: Theory and Algorithms.

Jan
23: Lab (in MGH 044). Interactive StateSpace Search.

4

Jan
26: Lecture (in MGH 231). Knowledge representation with ISA hierarchies.

Jan
28: Lecture (in MGH 231). Propositional logic, modus ponens, perfect induction, and resolution.

Jan
30: Lecture (in MGH 231). Predicate Logic. Definitions. Hornclause resolution. Assignment 2 due. Assignment 3 (Logical reasoning) given out.

5

Feb
2: Lecture (in MGH 231). Predicate Logic, continued

Feb
4: Lecture (in MGH 231). Predicate Logic, continued

Feb
6: Lecture (in MGH 231). Agent reasoning with modal logic.

6

Feb
9: Lecture (in MGH 231). Probabilistic inference with Bayes' rule. Bayes' networks. Assignment 3 due.

Feb
11: Lecture (in MGH 231). Building Bayes nets.

Feb
13: Midterm 1 (in MGH 231).

7

Feb
16: Presidents Day Holiday (no class) 
Feb
18: Lecture (in MGH 231). Introduction to vision: illusions; sampling and quantization, histograms, thresholding, the Hough transform.

Feb
20: Lab (in MGH 044). Image understanding.

8

Feb
23: Lecture (in MGH 231). Image understanding: edge detection, segmentation into regions; morphology. Assignment 4 (Developing AI Software) given out.

Feb
25: Lecture (in MGH 231). Image understanding: scene labeling, pattern recognition with perceptrons.

Feb
27: Lecture (in MGH 231). Neural networks and machine learning: perceptron training and backpropagation. Assignment 4 due. Begin projects.

9

Mar
2: Lab (in MGH 030). Expert systems using Horn clause resolution in PROLOG

Mar
4: Review session (in MGH 231).

Mar
6: Midterm 2 (in MGH 231).

10

Mar
9: Lecture (in MGH 231). Natural language understanding: contextfree grammars and parsing. Semantic grammars and Semantics: Case frames. Augmented transition networks and the STONE WORLD program.

Mar
11: Lecture (in MGH 231). Big issues and the future of AI: common sense, ontologies, dangers of AI, Asimov's rules of robotics, hopes for AI.

Mar
13: Lab (in MGH 044). Presentations and Demos. Those who can come early are invited to arrive at 11:30, in order to have more time for presentations and feedback.


Mar
19 (Thursday):
FINAL PROJECT DEMO SESSION 8:3010:20 in MGH 044
