Course: Artificial Intelligence
Room: Engineering 2108
- Tu: 12:15-1:00,1:30-3:00
- Th: 12:15-1:00,1:30-4:00
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 SlidesTo be read before the class for which they are listed. Empty spaces will be filled in, so check back frequently. RN 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, 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.
- 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)