Spring 2020 : Artificial Intelligence

Schedule

Lecture	Date	Topic	Instructor	Reading (R&N) and Slides
Lecture 1	Jan 14	Intro: AI as Automation of Cognitive Tasks Part 1	Anatole, Madhavi	Reading: Chapters 1 & 2 \| Slides [pdf]
Lecture 2	Jan 15	Intro: AI as Automation of Cognitive Tasks Part 2	Anatole	Reading: Chapters 1 & 2 \| Slides [pdf]
	Jan 20	No Class (Martin Luther King Jr Day)
Lecture 3	Jan 22	Uninformed Search	Madhavi	Reading: Chapters 3.1-3.4 \| Slides [pdf]
Lecture 4	Jan 27	Uninformed Search / Informed Search	Ralf	Reading: Chapters 3.5-3.6 \| Slides [pdf]
Lecture 5	Jan 29	Informed Search	Ralf	Reading: Chapters 3.5-3.6 \| Slides [pdf]
Lecture 6	Feb 3	Invited Talk: Knowledge-Based AI for Common Sense and Language Understanding	Scott Fahlman	Slides [pdf]
Lecture 7	Feb 5	Adversarial Search	Madhavi	Reading: Chapters 5.1-5.5 \| Slides [pdf]
Lecture 8	Feb 10	Constraint Satisfaction Problems	Madhavi	Reading: Chapters 6.1-6.4 \| Slides [pdf]
Lecture 9	Feb 12	Planning	Ralf	Reading: Chapters 10.1-10.3, 11.1-11.4 \| Slides [pdf]
Lecture 10	Feb 17	Invited Talk: Weighted A* and Anytime Informed Search	Maxim Likhachev	Slides [pdf]
Lecture 11	Feb 19	Invited Talk: Case Study of Planning for Autonomous Driving	Maxim Likhachev	Slides [pdf]
Lecture 12	Feb 24	AAAI Turing Award Talk	Sang, Nithin	Video [link]
Lecture 13	Feb 26	Discussion of AI Applications	Ralf	Slides [pdf]
Lecture 14	Mar 2	No Class
Lecture 15	Mar 4	Propositional/First Order Logic	Madhavi	Reading: Chapters 7, 8 \| Slides [pdf]
	Mar 9	Spring Break
	Mar 11	Spring Break
Lecture 16	Mar 16	No Class
Lecture 17	Mar 18	Decision Trees	Madhavi	Reading: Chapters 18.3 \| Slides
Lecture 18	Mar 23	Representation of Uncertain Knowledge	Anatole	Slides
Lecture 19	Mar 25	Belief Propagation	Anatole	Slides
Lecture 20	Mar 30	Approximate Probabilistic Reasoning	Anatole	Slides
Lecture 21	Apr 1	Probabilistic Reasoning over Time	Anatole	Slides
Lecture 22	Apr 6	Linear Models	Ralf	Slides
Lecture 23	Apr 8	Support Vector Machines	Ralf	Slides
Lecture 24	Apr 13	Invited Talk: Learning Causal Bayesian Networks from Data	Greg Cooper
Lecture 25	Apr 15	Neural networks	Madhavi	Slides
Lecture 26	Apr 20	Invited Talk	Jaime Carbonell
Lecture 27	Apr 22	Invited Talk: Deep Learning	Bhiksha Raj
Lecture 28	Apr 27	Invited Talk	TBA
Assignment Dates: Check Piazza for updates: Assigment 1: Jan 22 - Feb 12 Assigment 2: Feb 15 - Mar 5 Assigment 3: Mar 23 - Apr 6 Assigment 4: Apr 6 - Apr 20

Policies

Late Submission Policy

Each student will be allowed 5 total late days without penalty for the entire semester. No more than 3 late days can be used on any single assignment. Weekends and holidays are also counted as late days. Late submissions are automatically considered as using late days.
NOTE: Any assignment submitted more than 3 days past the deadline will get zero credit.

Extensions

In general, we do not grant extensions on assignments. We expect late days to help you with sufficient accommodation. However, in the case of severe medical or family emergencies, you may request an extension by emailing the TA at sangkeuc [a] cs [dot] cmu [dot] edu – also copy instructors in the email . The email should be sent as soon as you are aware of the conflict.

Academic Integrity Policies

(The following policies are adapted from Prof. Roni Rosenfeld’s 10-601 Spring 2016 Course Policies.)

Collaboration among Students

The purpose of student collaboration is to facilitate learning, not to circumvent it. Studying the material in groups is strongly encouraged. It is also allowed to seek help from other students in understanding the material needed to solve a particular assignment problem, provided no written notes are shared, or are taken at that time. The actual solution must be done by each student alone. You must indicate on each assignment with whom you collaborated and on what part of the assignment.
The purpose of programming assignments in this course is to make sure you truly understand the relevant techniques. For this reason, in the case of programming assignments all code must be written by each student alone and must be implemented "from scratch". We will strictly enforce this policy, by carefully inspecting your code using sophisticated detection techniques.
Collaboration without full disclosure will be handled severely, in compliance with CMU’s Policy on Academic Integrity.

Previously Used Assignments

Some of the homework assignments used in this class may have been used in prior versions of AI class at CMU, or in classes at other institutions, or elsewhere. Solutions to them may be available on the internet. It is explicitly forbidden to search for these problems or their solutions on the internet. You must solve the homework assignments completely on your own. We will be actively monitoring your compliance. Collaboration with other students who are currently taking the class is allowed, but only under the conditions stated above.

Duty to Protect One’s Work

Students are responsible for pro-actively protecting their work from copying and misuse by other students. If a student's work is copied by another student, the original author is also considered to be at fault and in gross violation of the course policies. It does not matter whether the author allowed the work to be copied or was merely negligent in preventing it from being copied. When overlapping work is submitted by different students, both students will be punished. To protect future students, do not post your solutions publicly, neither during the course nor afterwards.

Penalties for Violations of Course Policies

Any violation of course policies will always be reported to the respective authorities (your Program Head, etc.) as an official Academic Integrity Violation and will carry severe penalties.

The penalty for the first violation is a one-and-a-half letter grade reduction. For example, if your final letter grade for the course was to be an A, it would become a B-.
The penalty for the second violation is failure in the course, and can even lead to dismissal from the university.

Accommodations for Students with Disabilities:

If you have a disability and have an accommodations letter from the Disability Resources office, we encourage you to discuss your accommodations and needs with us as early in the semester as possible. We will work with you to ensure that accommodations are provided as appropriate. If you suspect that you may have a disability and would benefit from accommodations but are not yet registered with the Office of Disability Resources, we encourage you to contact them at access@andrew.cmu.edu.

Statement of Support for Students’ Health & Well-being

Take care of yourself. Do your best to maintain a healthy lifestyle this semester by eating well, exercising, avoiding drugs and alcohol, getting enough sleep and taking some time to relax. This will help you achieve your goals and cope with stress.
All of us benefit from support during times of struggle. You are not alone. There are many helpful resources available on campus and an important part of the college experience is learning how to ask for help. Asking for support sooner rather than later is often helpful.
If you or anyone you know experiences any academic stress, difficult life events, or feelings like anxiety or depression, we strongly encourage you to seek support. Counseling and Psychological Services (CaPS) is here to help: call 412-268-2922 and visit their website at http://www.cmu.edu/counseling/. Consider reaching out to a friend, faculty or family member you trust for help getting connected to the support that can help.

If you have questions about this or your coursework, please let us know. Thank you, and have a great semester.

Spring 2020, IITP Executive Education Course

Artificial Intelligence: Representation and Problem Solving

Overview