GRS CS 640 Artificial Intelligence - Fall 2024

Seeing Me on Zoom:
Please feel free to stop by my zoom office hours. I'm happy to talk with you about the course, research in AI, computer vision, machine learning, your plans for the future, or anything else. Check out my personal web page (in need of updating...) to get to know me a little.

Responsibilities of the Teaching Fellow and Co-Instructors:
The TF Stan Lai is responsible for teaching the two laboratory sections and helping you out during his office hours. He will also help me design the written homework and programming projects, and he will manage the graders. Please contact the TF first if you have questions about your homework grades. The Co-Instructors Yiwen Gu and Mahir Patel are responsible for co-teaching the lectures and provided help with the course materials outside the classroom. Please see them in their office hours if you have any questions about the lecture materials.

Piazza Q&A Policy
Piazza is our primary communication forum. Please use it to contact us for any course-related questions. But note there is a 24-hour timer on your public questions to encourage other students to answer questions before the TF will able to see your question. You can create a private post on Piazza if necessary (e.g., personal concerns, questions revealing your answers, etc.).
Only contact us via email for extremely urgent questions .

Learning Objectives

Our goal is to learn about computer systems that exhibit intelligent behavior, in particular, perceptual, reasoning, and robotic systems. Topics include human computer interfaces, computer vision, robotics, machine learning, game playing, knowledge representation, planning, pattern recognition, and natural language processing.

Prerequisites: Calculus, Linear Algebra, Discrete Mathematics, Coding (Python, Java, or C++), Datastructures.

The syllabus below will be updated throughout the semester with lecture notes and reading materials. Links to homework assignments will come alive when the assignment is ready for you to work on. The first assignment will give instructions on how you can submit your solution using Gradescope.

Textbook: You are not required to buy a text book. For background reading, you may want to check out the following two books from which we will use materials: and Artificial Intelligence A Modern Approach by Russell and Norvig, Edition 2, Edition 3, or Edition 4. and Artificial Intelligence by Patrick H. Winston, Both books provide a fundamental background of AI. We will follow, for example, the chapters about Expert Systems and Neural Nets in the Winston book and about Robotics in the Russell and Norvig book, and then augment the materials with newer articles, especially with regards to deep learning.

Class Engagement: Come to class and participate regularly. Reading the assigned texts and listening in lectures and labs will only give you a "passive understanding" of the material. We encourage discussions during lectures and labs to help you acquire an "active understanding" of the material so that you can evaluate existing computer systems critically and learn to develop your own creative solutions. Another way of interacting with the instructors and your classmates is answer students' questions on Piazza.

Homework: Guidelines for submission will be provided with each assignment. We allow teams of two students for the programming assignments. Each student must prepare their own written homework. Late solutions will be levied a late penalty of 20% per day (up to three days). After three days, no credit will be given.

Course Team Project: The project involves developing an AI system in a team. More details will be announced. We will discuss your project's scope, design, and presentation with you in class, labs, and office hours. The project has three due dates: (1) your plan, (2) your initial results, and (3) your final results (see schedule).
Please read the instructions for the course project carefully. They will be published on piazza.

Colloquia: You are encouraged to attend the weekly seminar series, Wednesdays, 1-2 pm, Room CDS 701, of the BU AI Research Initiative. Please sign up for the AIR email list to get weekly updates on events. To do that, join the Google group air_seminar_public with your BU account. You may also check out AI talks in other departments at Boston University and at other local universities. A partial list includes: BU Faculty of Computing and Data Sciences, BU College of Engineering Seminar Calendar, CSAIL lab at MIT, Northeastern College of Computer and Information Science, Tufts Department of Computer Science Colloquium.

Talk Reports: You must attend at least three talks on subjects related to Artificial Intelligence and write a summary on each talk.
We recommend attending the Distinguished Seminar by Marc Raibert on Making Robots Smarter in Body in Mind, Friday, September 13, 3-4 pm, CDS 1750.
Your seminar writeup should give a problem definition, summarize the methods and results, discuss the work critically, and also briefly explain how the work relates to material discussed in class. One page is sufficient, do not submit more than two pages. You may not work in a team but produce your own independent writeup.
Check your text for typographical and grammatical errors, especially if English is not your native language. Use tools to check for error, e.g., grammarly.com, ChatGPT. You must acknowledge use of these tools. You will lose points if you simply copy the speaker's abstract, your review is late, contains typographical and grammatical errors, does not contain a discussion, and does not contain a statement how the talk relates to class material.

Exams: There will be two exams on the material discussed in the class and practiced with homeworks. The exams will be quite easy for students who attend class, participate in our discussions, and keep up with homework assignments and programming projects. The midterm exam will be on October 8, 2024, final exam will be during the finals period in December and will be determined by the University.

Grading Policy: Your final grade will be determined as follows:

You are encouraged to discuss course materials on Piazza and allowed to collaborate with classmates on the solution of the homework. If you do, you must acknowledge your collaborators. Each student must submit his or her own electronic version of the solutions. While the code of collaborating students may be the same, comments explaining the code and answers to exercises should not not be identical but based on the individual student's work. If you use algorithms or code that are not your own original work and that were not provided in class or discussed in the reading materials, or if you use ChatGPT or similar AI tools for help with your solutions, you must give a detailed acknowledgment of your source. You will not lose points for using tools but you will use all points if you use tools and do not acknowledge them.

Cheating and plagiarism are not worthy of Boston University students. We expect you to abide by the rule stated above and the standards of academic honesty and computer ethics policy described in http://www.bu.edu/computing/ethics/ and http://www.bu.edu/academics/policies/academic-conduct-code.

Artificial Intelligence is a very exciting topic, and the course should be fun and not too much of a struggle for you. Make sure that you have had the prerequisites. Depending on your level of programming experience and/or mathematics background, the course may be challenging for you. If you do not understand the material, ask for help immediately. Ask questions in class. If one student is confused about something, then maybe others are also confused and grateful that someone asked. Please interact with the instructors and TF for help. Our task is to help you learn a very interesting topic. Also interact on Piazza with your classmates. This is your chance to build your network of AI colleagues.

Course Schedule

The lecture schedule may change depending on the time spent on each topic and whether alternative subjects are discussed. Suggestions for additional topics are welcome!

Links

Check out http://www.cs.bu.edu/faculty/betke/links.html if you are looking for a job, or if you are interested in research related to AI and computer vision. You will find a list of links to conferences, journals, research groups, and companies.

Margrit Betke, Professor Computer Science Department Boston University URL: http://www.cs.bu.edu/faculty/betke Last updated: September 4, 2024