Approximately 40% of the course grade will be determined by
individual assignments, 45% by the midterm and final exams, 15% by your
term project. Class attendance and participation will
also be taken into account in
determining the
course grade. Late
assignments may be discounted, and very late assignments may be
discarded. In order to achieve a passing grade, students must
pass all three portions of the course (homework, exams, term project).
Academic Honesty: The individual assignments must be each student's
own
work. Any group projects assigned must be the work of the
students in
the group. Plagiarism or copying will result in official
University
disciplinary review.
Security is an important aspect of computer science. Students are
expected to protect their work
from plagiarists.
| WEEK |
Topics
|
Readings |
||
| 1 1/10 & 12 2 1/17 & 19 3 1/24 & 26 4 1/31 & 2/2 |
Intelligent Agents; Python Introduction Using logic to represent an agent's knowledge Automated reasoning Automated reasoning (cont.) |
RN 1.3 & 1.4,Ch 2 |
||
| RN 7.1-7.5; Ch. 8 (review) Ontology 101 paper |
||||
| RN Ch. 9.1-9.3 |
||||
| RN 9.4-9.5 |
||||
| 5 2/7 & 9 |
Search and Heuristics |
RN Ch. 3 |
||
| 6 2/14 & 16 7 2/21 2/23 8 2/28 3/1 9 3/6 & 8 |
Planning in AI/Ontology Design AI for games: Guest Lecture Prof. Magy Seif El-Nasr Ontology Design (cont.) and the Protege Tool Discuss term projects/Review for midterm Midterm Exam SPRING BREAK NO CLASSES |
RN Ch. 10, 11.1, 11.2.1, 11.2.2 Ontology 101 paper (review) TBA |
||
| 10 3/13 & 15 11 3/20 & 22 12 3/27 & 29 13 4/3 & 5 14 4/10 |
Probabilistic Reasoning and Bayes' Nets Machine Learning 1: Classification Learning Decision Tree Learning Nearest Neighbor Methods Machine Learning 2: Perceptrons and Neural Nets Natural Language and ML: Part of Speech Tagging Formal Grammars for Natural Language |
RN Ch. 13, 14.1 - 14.4 RN 18.1-18.5 RN 18.7, RN Ch. 22 RN Ch. 23.1-23.3 |
||
| 4/12 |
Student Presentations |
|||
| 4/17 |
Student Presentations |
|||
| TBA |
Final exam |