Fall 2019, IITP Executive Education Course

Overview

Lecture

Monday + Wednesday 5:00 - 6:20pm WEH 2302

Recitation

Friday 5:00 - 6:00pm WEH 2302

Instructors

Teaching Assistants

Grading

Midterm (20%), Final (20%), Written homework (30%), Programming homework (30%)

Course Materials

We recommend Artificial Intelligence: A Modern Approach, Third Edition (R&N) textbook for this course. Slides will be posted periodically here

Announcements + Q&A

We will use Piazza for questions and any course announcements.

Submitting Assignments

Students will turn in their homework electronically using Gradescope.

Schedule

Lecture	Date	Topic	Instructor	Reading and Slides
Lecture 1	Aug 28	Introduction	Madhavi	Reading: R&N Chapters 1 & 2 | Slides [pdf]
	Sept 2	Holiday: No class
Lecture 2	Sept 4	Uninformed Search	Madhavi	Reading: R&N Chapters 3.1-3.4 | Slides [pdf]
Lecture 3	Sept 9	Uninformed, Informed Search	Madhavi	Reading: R&N Chapters 3.5-3.6 | Slides [pdf]
Lecture 4	Sept 11	Informed Search	Madhavi	Reading: R&N Chapters 3.5-3.6 | Slides [pdf]
Lecture 5	Sept 16	Adversarial Search	Madhavi	Reading: R&N Chapters 5.1-5.2 | Slides [pdf]
Lecture 6	Sept 18	Adversarial Search	Madhavi	Reading: R&N Chapters 5.3-5.5 | Slides [pdf]
Lecture 7	Sept 23	Pioneers in AI@CMU (Talk 1): Knowledge-Based AI for Common Sense and Language Understanding	Prof. Scott Fahlman	Reading: | Slides [pdf]
Lecture 8	Sept 25	Constraint Satisfaction Problems	Madhavi	Reading: R&N Chapters 6.1-6.4 | Slides [pdf]
Lecture 9	Sept 30	Logical Agents, Propositional Logic	Madhavi	Reading: R&N Chapters 7.1-7.5 | Slides [pdf]
Lecture 10	Oct 2	Pioneers in AI@CMU (Talk 2)	Prof. Raj Reddy
Lecture 11	Oct 7	First Order Logic	Madhavi	Reading: R&N Chapters 8.1-8.3 | Slides [pdf]
Lecture 12	Oct 9	Review of Material for Midterm	Madhavi/ Sanket
	Oct 14	MIDTERM EXAM
Lecture 13	Oct 16	Knowledge Representation: Ontologies	Madhavi	Reading: R&N Chapters 12 | Slides [pdf]
Lecture 14	Oct 21	Probabilistic Reasoning	Madhavi	Reading: | Slides [pdf]
Lecture 15	Oct 23	Machine Learning	Jaime	Reading: Class Notes [pdf]
Lecture 16	Oct 28	Machine Learning, continued	Madhavi	Reading: | Slides [pdf]
Lecture 17	Oct 30	Bayesian Networks	Madhavi	Reading: | Slides [pdf]
Lecture 18	Nov 4	Bayesian Networks, continued	Madhavi	Reading: | Slides [pdf]
Lecture 19	Nov 6	Markov Decision Process	Madhavi	Reading: | Slides[pdf]
Lecture 20	Nov 11	Hidden Markov Models	Madhavi	Reading: | Slides [pdf]
Lecture 21	Nov 13	Reinforcement Learning	Madhavi/ Sanket	Reading: | Slides [pdf]
Lecture 22	Nov 18	Ruled-Based Systems	Jaime	Reading: | Slides [pdf]
Lecture 23	Nov 20	Pioneers in AI@CMU (Talk 3)	Prof. Jaime Carbonell	Reading: Class Notes [pdf]
Lecture 24	Nov 25	Explainable AI: Review of Recent papers		Reading: | Slides [pdf]
	Nov 27	Holiday: No class
Lecture 25	Dec 2	Transfer Learning and Active Learning	Jaime	Reading: | Slides [pdf]
Lecture 26	Dec 4	Review of Material for Final	Madhavi/ Sanket
	Dec 8	FINAL EXAM
Tentative Assignment Dates: Check Piazza for updates: Assigment 1: Out: Sept 4th -- Due Sept 18th Assigment 2: Out: Sept 24th -- Due Oct 8th Assigment 3: Out: Oct 21st -- Due Nov 4th Assigment 4: Out: Nov 11th -- Due Nov 25th

Exams

The course includes a midterm exam (Oct 14) and a final exam (Dec 11). Both the exams will be in class.

Assignments

There will be four assignments for the course with tentative due dates as mentioned above.
Please write all assignments in LaTeX using the provided style file (check Piazza). 60% of course grades will be based on 4 assignments (15% each).
The assignments are to be done by each student individually. You may discuss the general idea of the questions with anyone you like, but your discussion may not include the specific answers to any of the problems.

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 svmehta [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.

1. 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-.
2. 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.