CS63 Artificial Intelligence
Fall 2022
Course Information
| Class: | Mon., Wed., Fri. 10:30 - 11:20PM, Singer 222 |
|---|---|
| Lab A: | Friday 2:15 - 3:45PM, SCI 256 |
| Lab B: | Friday 4:00 - 5:30PM, SCI 256 |
| Professor: | Lisa Meeden |
|---|---|
| Email: | meeden at cs dot swarthmore.edu |
| Office: | Science Center 243 |
| Office Hours: | Wed. and Thur. 2:00-3:30 pm And any time my door is open |
| Communication: | Ed I will post important announcements here You can ask questions and answer other students' questions here |
Introduction
Artificial Intelligence (AI) is the branch of computer science that is concerned with the automation of intelligent behavior. Intelligent behavior encompasses a wide range of abilities. As a result, AI has become a very broad field that includes search, game playing, reasoning, planning, natural language processing, modeling human performance (cognitive science), machine learning, and robotics. This course will focus on a subset of these topics, specifically search and machine learning, while also drawing connections to cognitive science.
In search, we will see familiar techniques such as depth-first and breadth-first, as well as new techniques such as A*, minimax, and simulated annealing applied to AI problems. In machine learning, which is concerned with how to create programs that automatically learn from experience, we will explore reinforcement learning and neural networks. The first half of the semester will focus on search, and the second half of the semester will focus on machine learning.
Goals for the course
- Apply a variety of search techniques to AI problems.
- Apply a variety of machine learning techniques to AI problems.
- Compare and contrast the strengths and weaknesses of various AI approaches.
- Given a particular AI problem, determine an appropriate representation and approach for solving it.
- Understand and be able to respond to the common objections to the possibility of achieving AI.
- Understand the ethical implications and dangers of AI.
Grading
| 5% | Class Participation |
| 10% | Reading Journal |
| 27% | Labs |
| 24% | Exam 1, in lab 10/7/22 |
| 24% | Exam 2, in lab 11/18/22 |
| 10% | Final Project |
Reading
Rather than using a single textbook, we will be using materials from a variety of sources. Many of the materials will be available online. You only need to purchace the first text listed below by Melanie Mitchell, which is required.
- Artificial Intelligence: A Guide for Thinking Humans, by Melanie Mitchell
- Artificial Intelligence: A Modern Approach (3rd Edition), by Stuart Russell and Peter Norvig
- Artificial Intelligence: Foundations of Computational Agents (2nd Edition), by David Poole and Alan Mackworth
Our class meetings will be a combination of lecture and discussion. To be ready to participate in the discussion will require some preparation on your part. Most of this will consist of careful reading and reflection on the assigned reading through the use of a reading journal.
Preparation
You should check the class schedule and read any material that has been assigned for the week before coming to class on Monday morning. You will get the most out of the reading if you approach it as follows:
- Overview: You should quickly skim through the reading once to get an overview of the material to be covered, paying particular attention to subject headings and topic introductions. This should be very quick.
(Estimated time: 5 minutes) - In-Depth: Next, read the material closely. Try to understand what individual steps of algorithms are accomplishing. Not everything will make sense at this point, but hopefully many things will.
(Estimated time: 40 minutes) - Final Notes: After carefully reading the material, mentally review and try making a few notes to yourself about what you think are the most important concepts being covered, as well as any questions that you have.
(Estimated time: 5 minutes)
Journal
To help focus your efforts and give us a basis for discussion, you will be provided with a short list of questions to answer for each week's reading. Reflecting on your responses to the questions will help give you a deeper understanding of the most important concepts surrounding each topic.
Your responses are due by 8:00am Monday of the week where they will be discussed.
You will clone a reading journal repo containing markdown files for each week's reading. You will write your responses in the appropriate file and submit them via git (using add, commit, and push).
While these low-stakes writing assignments are technically informal, they must reflect a certain level of engagement and evidence of thinking seriously about the material. Responses will be graded using the following scale:
- PLUS: Exhibits exceptional clarity, insight, and/or creativity
- CHECK: Exhibits evidence of processing and studying concepts
- MINUS: Superficial response or insufficient evidence of engagement
- ZERO: Did not complete
Policies
Labs
Labs will be assigned on Friday, during the scheduled lab time, and will be due by the following Thursday before midnight. Even if you do not fully complete a lab, you should submit what you have done to receive partial credit.
You should work with a partner on all labs after lab 0. We will be using Teammaker to facilitate the creation of partnerships. Note that you must select a partner from within your lab section. If you and another student would like to partner together, simply select one another via Teammaker. If you would like to be assigned a random partner, you can select this option through Teammaker as well. For each lab assignment you must re-select partners. Thus you can try out a partnership one week, and then decide to try a different partnership the following week.
You have two late days that you may use on any lab, for any reason. If you are using a late day, you must contact me by email when to let me know.
Your late days will be counted at the granularity of full days and will be tracked on a per-student (NOT per-partnership) basis. That is, if you turn in an assignment five minutes after the deadline, it counts as using one day. For partnered labs, using a late day counts towards the late days for each partner. In the rare cases in which only one partner has unused late days, that partner's late days may be used, barring a consistent pattern of abuse.
If you feel that you need an extension on an assignment or that you are unable to attend class for two or more meetings due to a medical condition please let me know as soon as possible.
Academic Integrity
Academic honesty is required in all of your work. Under no circumstances may you hand in work done with (or by) someone else under your own name. Your code should never be shared with anyone; you may not examine or use code belonging to someone else, nor may you let anyone else look at or make a copy of your code. The only exception to this policy, is that you may freely share code with your lab partner.
You should not obtain solutions from students who previously took the course or copy code that can be found online. You may not share solutions after the due date of the assignment.
Failure to abide by these rules constitutes academic dishonesty and will lead to a hearing of the College Judiciary Committee. According to the Faculty Handbook: "Because plagiarism is considered to be so serious a transgression, it is the opinion of the faculty that for the first offense, failure in the course and, as appropriate, suspension for a semester or deprivation of the degree in that year is suitable; for a second offense, the penalty should normally be expulsion."
Discussing ideas and approaches to problems with others on a general level is fine (in fact, we encourage you to discuss general strategies with each other), but you should never read any other student's code or let another student read your code. All code you submit must be your own with the following permissible exceptions: code distributed in class and code given in the readings. In these cases, you should always include comments that indicate on which parts of the assignment you received help, and what your sources were.
Academic Accommodations
If you believe that you need accommodations for a disability, please contact the Office of Student Disability Services through email studentdisabilityservices@swarthmore.edu to arrange an appointment to discuss your needs. As appropriate, the Office will issue students with documented disabilities a formal Accommodations Letter. Since accommodations require early planning and are not retroactive, please contact the Office of Student Disability Services as soon as possible. For details about the accommodations process, visit the Student Disability Service Website. You are also welcome to contact me privately to discuss your academic needs. However, all disability-related accommodations must be arranged through the Office of Student Disability Services.
Schedule
| WEEK | DAY | ANNOUNCEMENTS | TOPIC & READING | LAB |
| 1 | Aug 29 | Introduction to AI
| ||
Aug 31 | ||||
Sep 02 | ||||
| 2 | Sep 05 | Labor Day | ||
Sep 07 | Ben Mitchell guest lecture | State space search
| ||
Sep 09 | ||||
| 3 | Sep 12 | Local search
| ||
Sep 14 | ||||
Sep 16 | ||||
| 4 | Sep 19 | Game tree search
| ||
Sep 21 | ||||
Sep 23 | ||||
| 5 | Sep 26 | Monte Carlo Search
| ||
Sep 28 | ||||
Sep 30 | ||||
| 6 | Oct 03 | Evaluating Classical AI
| Exam 1 in lab | |
Oct 05 | ||||
Oct 07 | ||||
Oct 10 | Fall Break | |||
Oct 12 | ||||
Oct 14 | ||||
| 7 | Oct 17 | Perceptrons and Neural Networks
| ||
Oct 19 | ||||
Oct 21 | ||||
| 8 | Oct 24 | Deep Learning
| ||
Oct 26 | ||||
Oct 28 | ||||
| 9 | Oct 31 | Reinforcement Learning
| ||
Nov 02 | ||||
Nov 04 | ||||
| 10 | Nov 07 | Genetic Algorithms
| ||
Nov 09 | ||||
Nov 11 | ||||
| 11 | Nov 14 | Evaluating Machine Learning
| Exam 2 in lab | |
Nov 16 | ||||
Nov 18 | ||||
| 12 | Nov 21 | Final Project | No lab | |
Nov 23 | ||||
Nov 25 | Thanksgiving | |||
| 13 | Nov 28 | Philosophy of AI
| Project checkpoint | |
Nov 30 | ||||
Dec 02 | ||||
| 14 | Dec 05 | Future of AI
| Project continued | |
Dec 07 | ||||