If you have a question about getting into the course, please contact the CS Enrollment Administrator at cs-course-enroll@cornell.edu. Most questions about enrollment are not specific to this course and you'll find the answers at:
Our strong advice is to not use technology in the classroom. However, some students will want to do things like use a tablet to take notes on the PDFs of slides. As a result the policy for this course is as follows:
You can use technology only if you are seated in the back rows of either side section of the lecture hall. If you use technology otherwise you will be asked to turn it off.(For completeness, should the instructor ask you to do use technology - such as to answer an online poll - you are of course allowed to use it. When completed the technology must be put away.)
If you believe you have special circmstances that aren't covered by this policy please contact the instructor.
Absolute integrity is expected of every Cornell student in all academic undertakings. Integrity entails a firm adherence to a set of values, and the values most essential to an academic community are grounded on the concept of honesty with respect to the intellectual efforts of oneself and others. Academic integrity is expected not only in formal coursework situations, but in all University relationships and interactions connected to the educational process, including the use of University resources. ... A Cornell student's submission of work for academic credit indicates that the work is the student's own. All outside assistance should be acknowledged, and the student's academic position truthfully reported at all times. In addition, Cornell students have a right to expect academic integrity from each of their peers.Any distribution or dissemination of course assignment information (such as copies of exams, solutions, etc.) to students not taking the course - whether to students in a subsequent offering of the course, via a website, or any other circmstance - is a violation of the academic intregity policy for this course. We will pursue strong negative consequences for academic integrity violations.
1/23 | Overview | ||
1/25 | Uninformed Search | Russell & Norvig Ch. 3.1-4 | |
1/25 | Homework 1 out | Due 2/9 | |
1/30 | Informed Search | Russell & Norvig Ch. 3.5-6 | |
2/1 | Local search | Russell & Norvig Ch. 4.1-2 | |
2/6 | Game Trees: Minimax | Russell & Norvig Ch. 5.1-3 | |
2/8 | Game Trees: Expectimax, Utilities | Russell & Norvig Ch. 5.5, 16.1-16.3 | |
2/8 | Homework 2 out | Due 2/23 | |
2/9 | Homework 1 due | ||
2/13 | Markov Decision Processes 1 | Russell & Norvig Ch. 17.1-3 | |
2/15 | Markov Decision Processes 2 | Russell & Norvig Ch. 17.1-3 | |
2/20 | Reinforcement Learning 1 | Russell & Norvig Ch. 21 | |
2/22 | Reinforcement Learning 2 | Russell & Norvig Ch. 21 | |
2/23 | Homework 2 due | ||
2/25-2/28 | February Break | ||
3/1 | Probability | Russell & Norvig Ch. 12.1-6 | |
3/1 | Homework 3 out | Due 3/10 | |
3/6 | Bayes Nets | Russell & Norvig Ch. 13.1-2 | |
3/8 | Independence | Russell & Norvig Ch. 13.1-2 | |
3/10 | Homework 3 due | ||
3/13 | Exact Inference | Russell & Norvig Ch. 13.3 | |
3/15 | Sampling Methods | Russell & Norvig Ch. 13.4 | |
3/16 | Prelim Exam | 7:30pm, location TBD | |
3/20 | Hidden Markov Models 1 | Russell & Norvig Ch. 14.1-3 | |
3/20 | Homework 4 out | Due 3/31 | |
3/22 | Hidden Markov Models 2 | Russell & Norvig Ch. 14.1-3 | |
3/27 | Decision Networks / VPI | Russell & Norvig Ch. 16.5-6 | |
3/29 | Machine learning 1 | Russell & Norvig Ch. 20.1-20.2.2 | |
3/31 | Homework 4 due | ||
4/1-4/9 | Spring Break | ||
4/10 | Machine learning 2 | Russell & Norvig Ch. 18.6.3 | |
4/10 | Homework 5 out | Due 4/25 | |
4/12 | Machine learning 3 | Russell & Norvig Ch. 18.8 | |
4/17 | Machine learning 4 | ||
4/19 | Propositional Logic 1 | Russell & Norvig Ch. 7.1-4 | |
4/24 | Propositional Logic 2 | Russell & Norvig Ch. 7.5 - 7.7 | |
4/24 | Homework 6 out | Due 5/9 | |
4/25 | Homework 5 due | ||
4/26 | First-Order Logic | Russell & Norvig Ch. 9.1-5 | |
5/1 | Planning | Russell & Norvig Ch. 11.1-3 | |
5/3 | Special Topics in AI 1 | ||
5/8 | Special Topics in AI 2 | ||
5/9 | Homework 6 due | ||
TBD, 5/13-5/20 | Final Exam | ||