Each student is responsible for his/her own work. The standard departmental rules for academic dishonesty apply to all assignments in this course. Collaboration on homeworks and programs should be limited to answering questions that can be asked and answered without using any written medium (e.g., no pencils, pens, or email). This means that no student should read any code written by another student.
INTRODUCTION
Sept 29 AI = the design of rational agents [1.1, 1.4]
Oct 1 Structure of intelligent agents and environments [2 (all)]
Simple reflex agents, agents with memory, goal-based agents,
utility-based agents.
3 Search-based agents: systematic search. [3.1, 3.2, 3.3, 3.4, 4.1]
6 Local search [4.3, 4.4]
8 Propositional Logic and the WUMPUS world [7.1,7.2,7.3,7.4,7.7
(except the circuits part)
10 First-Order Logic [8.1, 8.2, 8.3]
13 Introduction to Probability [13]
15 Probability continued
17* FIRST MIDTERM EXAM
20 Probabilistic Reasoning [14.1,14.2,14.3,14.4]
22 Probabilistic Learning [20.2]
24 N-Gram Language Models [23.1]
27 Statistical Learning and Spam Filtering [20.1,20.2]
29 Probabilistic Reasoning over Time [15.1,15.2]
31 Hidden Markov Models [15.3]
Nov 3 Particle filters [15.5]
5 Speech recognition [15.6]
7 Statistical Learning with Hidden Data: EM [20.3]
10 EM for Hidden Markov Models [20.3]
12* Probabilistic language processing and IR [23.1,23.2]
14 Natural language understanding [22 all]
17 Natural language understanding (continued)
19 Information Extraction and Machine Translation [23.3,23.4]
21* SECOND MIDTERM EXAM
24 Computer Vision [24 all]
26 Computer Vision (continued)
Dec 1 Robotics [25.1, 25.4, 25.6]
3 Robotics (continued)
5 Philosophical Foundations and the future of AI [26]
8 FINAL PROJECTS DUE 9:30am