Course: Artificial Intelligence
COSC4550/COSC5550
Tu/Th 11:00am-12:15pm
Room: Engineering 2108
Office hours:
- Tu: 12:15-1:00,1:30-3:00
- Th: 12:15-1:00,1:30-4:00
Syllabus (pdf)
AI Challenges (Homework Assignments)
- Number 1 Due Sept. 11th (12th by 5am).
- Number 2 Due Sept. 18th (19th by 5am).
- Number 3 Due Sept. 25th (26th by 5am).
- Number 4 Due Oct. 9th (10th by 5am).
- Number 5 Due Oct. 23rd (24th by 5am).
Reading Assignments & Lecture Slides
To be read before the class for which they are listed. Empty spaces will be filled in, so check back frequently. RN[1] means chapter one in Russell & Norvig. RN[3-3.4] means the beginning of chapter 3 through the end of chapter 3.4. RN[3.5-end] means chapter 3.5 through the end of chapter 3. RN[1.35-end, 2-2.3] means read both 1.35 through the end of chapter one and Chapter 2 through and including section 2.3. You do not have to read the historical notes section at the end of each chapter, although please do if you find them interesting. Note: some reading assignments are for graduate students only, and are marked as such. Lecture slides for each day will be posted ASAP after the lecture as a link after the reading assignment.
Note: if links to slides do not work, that is because they have not been posted yet.
Tu, 8/30: No reading. First day of class. (slides)
Th, 9/01: RN[1-1.3.4] (22 pages) (slides)
Tu, 9/06: RN[1.35-end, 2-2.3] (23 pages)(slides)
Th, 9/08: RN[2.4-end] (13) (slides)
Tu, 9/13: RN[3-3.2] (15 pages) (slides)
Th, 9/15: RN[3.3…3.4.4] (13 pages) (slides)
Tu, 9/20: RN[3.5-end] (16 pages) (slides)
Th, 9/22: Catch up on anything unread (slides)
Tu, 9/27: RN[4-4.2] (13 pages) (slides)
Th, 9/29: RN[5-5.3] (10 pages) (slides)
Tu, 10/04: RN[5.4, 5.5, first two paragraphs of 5.6, 5.6.2-end ] (17 pages) (slides)
Th, 10/06: RN[13-13.2] (10 pages) (slides)
Tu, 10/11: RN[13.3-end] (13 pages) (slides)
Th, 10/13: RN[14-14.2] (8 pages) (slides)
Tu, 10/18: RN[14.3-14.4] (12 pages) (slides)
Th, 10/20: RN[14.5] (9 pages) (slides)
Tu, 10/25: RN[14.6-end] (12 pages) (slides)
Th, 10/27: RN[15-15.3] (18 pages) (slides)
Tu, 11/01: RN[15.4-end] (19 pages) (slides)
Th, 11/03: RN[17-17.2] (10 pages) (slides)
Tu, 11/08: RN[17.3-17.4] (10 pages) (slides)
Th, 11/10: RN[18-18.3.4] (11 pages) (slides)
Tu, 11/15: Midterm
Th, 11/17: RN[18.35-18.5] (12 pages) (slides)
Tu, 11/22: RN[18.6] (9 pages) (slides)
Th, 11/24: No class. University holiday.
Tu, 11/29: RN[18.7] (10 pages) (slides)
Th, 12/01: (slides)
- RN [18.8] (7 pages)
- Lee, Grosse, Ranganath, & Ng Unsupervised learning of hierarchical representations with convolutional deep belief networks. Communications of the ACM 54, 95–103 (2011).
- Hinton, G. E. & Salakhutdinov, R. R. Reducing the dimensionality of data with neural networks. Science (New York, N.Y.) 313, 504–7 (2006).
Tu, 12/06: Watch video: Andrew Ng: Unsupervised Feature Learning and Deep Learning. 2011. Graduate students also watch The next generation of Neural Networks, by Hinton. (slides)
Th, 12/08: RN[21-end] Graduate students also read RN[7-end] (slides)
Tuesday, 12/13: 10:15 am - 12:15 pm Final Exam Slot (Attendance Required)