Bryn Mawr College
CMSC 373: ARTIFICIAL INTELLIGENCE
Prof. Deepak Kumar
202 Park Science Building
dkumar at brynmawr dot edu
Lecture Hours: Mondays & Wednesdays, 10:10a to 11:30a
Room: Park 245
Lab: Mondays 11:40a to 1:00p in Room 230
Office Hours: Tuesdays from 1:00p to 2:00p, Wednesdays from 1:00p to 2:00p (or by appointment)
Course Description (from the Course Catalog): Survey of Artificial Intelligence (AI), the study of how to program computers to behave in ways normally attributed to “intelligence” when observed in humans. Topics include heuristic versus algorithmic programming; cognitive simulation versus machine intelligence; problem-solving; inference; natural language understanding; scene analysis; learning; decision-making. Topics are illustrated by programs from literature, programming projects in appropriate languages
and building small robots.
Note: The CS Departments at Bryn Mawr and Haverford now offers courses on several AI topics: Foundations of Data Science (CMSC 260), Computational Linguistics (CMSC 325), Cognitive Science (CMSC371), Machine learning (CMSC380), etc. These topics will not be covered in this course (in much detail). The focus will be on the foundations of AI that will prepare you to dig deeper into current and more advanced research areas of AI.
Here is what we plan to learn this semester:
August 30: First lecture
September 29: Exam 1
November 8: Exam 2
December 8: Exam 3
Creating a Welcoming Environment
All members of the Instruction Staff are dedicated to the cause of improving diversity, equity, and inclusion in the field of computing, and to supporting the wellness and mental health of our students.
Diversity and Inclusion
It is essential that all members of the course community – the instructor, TAs, and students – work together to create a supportive, inclusive environment that welcomes all students, regardless of their race, ethnicity, gender identity, sexuality, or socioeconomic status. All participants in this course deserve to and should expect to be treated with respect by other members of the community.
Class meetings, lab sessions, office hours, and group working time should be spaces where everyone feels welcome and included. In order to foster a welcoming environment, students of this course are expected to: exercise consideration and respect in their speech and actions; attempt collaboration and consideration, including listening to opposing perspectives and authentically and respectfully raising concerns, before conflict; refrain from demeaning, discriminatory, or harassing behavior and speech.
Additionally, your mental health and wellness are of utmost importance to the course Instruction Staff, if not the College as a whole. All members of the instruction staff will be happy to chat or just to listen if you need someone to talk to, even if it’s not specifically about this course.
If you or someone you know is in distress and urgently needs to speak with someone, please do not hesitate to contact BMC Counseling Serices: 610-526-7360 (610-526-7778 nights and weekends). If you are uncomfortable reaching out to Counseling Services, any member of the Instruction Staff will be happy to contact them on your behalf.
We understand that student life can be extremely difficult, both mentally and emotionally. If you are living with mental health issues such as anxiety, depression, ADHD, or other conditions that may affect you this semester, you are encouraged to discuss these with the Instructor. Although the details are up to you to disclose, the Instruction Staff will do their best to support and accommodate you in order to ensure that you can succeed this course while staying healthy.
- Assignment#1 is posted (Due on Wednesday, Septembe 14): Click here for details.
- Assignment#2 is posted (Due on Wednesday, Septembe 22): Click here for details.
- Week 1 (August 30, September 1)
August 30: What is AI? History, Foundations, Examples: Overview. Class lottery (if needed).
Slides: Introducing AI.
Read: Chapter 1 from R&N.
September 1: Intelligent Agents. Rational agents. Performance measures. Characterizing environments and agents.
Slides: Agents & Environments.
Read: Chapter 1 & 2 from R&N.
- Week 2 (September 6, 8)
September 6: Labor Day, no class.
September 8: Introducing Pac-man agent & Environment. Desinging Reflex Agents: sensory processing, feature vectors, if-then rules. Designing Model-based Agents (aka Problem Solving Agents). Example Problems. Formulating problems as Search Problems: states, actions, initial and goal states, state space, etc. Searching state-spaces using Search Algorithms.
Read: Sections 3.1 and 3.2 from R&N.
Pac-man Codebase download: search.zip
Assignment#1 is posted (Due on Wednesday, Septembe 15): Click here for details.
Play: Pac-man Google doodle.
- Week 3 (September 13, 15)
September 13: Model-based agents. Search algorithms for model-basd agents: Uninformed Search - Breadth-First Search, Depth-First Search, Depth-Limited Search, Iterative Deepening Search. Complexity of search. Towards informed search.
Read: Sections 3.3 & 3.4 from R&N.
Watch: Basic Roomba is a Reflex Agent (plus more).
September 15: BFS & DFS revistied. Depth-Limited Search. Iterative Deepening Search. Informed Search: using g(n) - uniform-cost search. Using heuristics, h(n) - A* Search. Admissibility and monotonicity of heuristics.
Read: Sections 3.4 & 3.5 from R&N.
Assignment#2 is posted (Due on Wednesday, Septembe 22): Click here for details.
- Week 4 (September 20, 22)
- Week 5 (September 27, 29)
September 29: Exam 1 is today.
- Week 6 (October 4, 6)
- Week 7 (October 11, 13)
- Week 8 (October 18, 20)
- Week 9 (October 25, 27)
- Week 10 (November 1, 3)
- Week 11 (November 8, 10)
November 8: Exam 2 is today.
- Week 12 (November 15, 17)
- Week 13 (November 22, 24)
- Week 14 (November 29, December 1)
- Week 15 (December 6, 8)
December 8: Exam 3 is today.
Attendance and active participation are expected in every class. Participation includes asking questions, contributing answers, proposing ideas, and providing constructive comments.
As you will discover, we are proponents of two-way communication and we welcome feedback during the semester about the course. We are available to answer student questions, listen to concerns, and talk about any course-related topic (or otherwise!). Come to office hours! This helps us get to know you. You are welcome to stop by and chat. There are many more exciting topics to talk about that we won't have time to cover in-class.
Although computer science work can be intense and solitary, please stay in touch with us, particularly if you feel stuck on a topic or project and can't figure out how to proceed. Often a quick e-mail, phone call or face-to-face conference can reveal solutions to problems and generate renewed creative and scholarly energy. It is essential that you begin assignments early, since we will be covering a variety of challenging topics in this course.
There will be 5-7 assignments (both written and in-class presentations), weighted equally in the final grading. Assignments must be submitted according to the Assignment Submission instructions.
All graded work will receive a grade: 4.0, 3.7, 3.3, 3.0, 2.7, 2.3, 2.0, 1.7, 1.3, 1.0, or 0.0. At the end of the semester, final grades will be calculated as a weighted average of all grades according to the following weights:
Incomplete grades will be given only for verifiable medical illness or other such dire circumstances.
Submission and Late Policy
All work must be turned in as specified at the start of the class period it is due. Any submission received after the first 10 minutes of the start of the class will be considered late (i.e. no credit).
No assignment will be accepted after it is past due.
No past work can be "made up" after it is due.
No regrade requests will be entertained one week after the graded work is returned in class.
Any extensions will be given only in the case of verifiable medical excuses or other such dire circumstances, if requested in advance and supported by your Academic Dean.
There will be three exams in this course. The exams will be closed-book and closed-notes. The exams will cover material from lectures, assignments, and assigned readings (including topics not discussed in class).
We encourage you to discuss the material and work together to understand it. Here are our thoughts on collaborating with other students:
If you have any questions as to what types of collaborations are allowed, please feel free to ask.
Academic Support Services
Bryn Mawr College offers a wide array of resources to help students be successful. The support services listed below can help Bryn Mawr students to:
For more information, please visit: Bryn Mawr College Academic and Student Support Services
Created on August 9, 2021.