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.
Much of the reading for this class will come from the following textbook, which is available online:
Artificial Intelligence: Foundations of Computational Agents, David Poole and Alan Mackworth
Additional online readings will be provided for some topics. To prepare for reading quizzes, you should read the assigned pages prior to the class meeting on which they are assigned. Be sure to look at the schedule to find the reading assignments for each week.
| 5% | Class Participation |
| 10% | Reading Quizzes |
| 35% | Labs |
| 25% | Exam 1, in lab on Feb. 23 |
| 25% | Exam 2, in lab on Apr. 12 |
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. This includes, but is not limited to, obtaining solutions from students who previously took the course or 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, code found in the course text book, and code worked on with a partner. In these cases, you should always include comments that indicate on which parts of the assignment you received help, and what your sources were.
Labs will be assigned on Tuesdays, during the scheduled lab time, and will be due on Mondays by midnight. Even if you do not fully complete a lab, you should submit what you have done to receive partial credit.
Late labs will only be accepted if you contact me at least a day before the deadline with a legitimate reason for needing extra time.
If you believe that you need accommodations for a disability, please contact Leslie Hempling in the Office of Student Disability Services (Parrish 113) or email lhempli1@swarthmore.edu to arrange an appointment to discuss your needs. As appropriate, she will issue students with documented disabilities a formal Accommodations Letter. Since accommodations require early planning and are not retroactive, please contact her as soon as possible. For details about the accommodations process visit Student Disability Services. 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.
| WEEK | DAY | ANNOUNCEMENTS | TOPIC & READING | LAB |
| 1 | Jan 18 | No class: MLK Day | Introduction to AI
| Lab 0: Maze search |
Jan 20 | ||||
Jan 22 | ||||
| 2 | Jan 25 | State Space Search | Lab 1: Traffic Jam | |
Jan 27 | ||||
Jan 29 | ||||
| 3 | Feb 01 | Drop/add ends | Local Search
| Lab 2: TSP |
Feb 03 | ||||
Feb 05 | ||||
| 4 | Feb 08 | Game Search
| Lab 3: Hex Game | |
Feb 10 | ||||
Feb 12 | ||||
| 5 | Feb 15 | Monte Carlo Search
| Lab 4: General Game Playing | |
Feb 17 | ||||
Feb 19 | ||||
| 6 | Feb 22 | Review for Exam 1 | Introduction to Machine Learning
| Exam 1 in lab |
Feb 24 | ||||
Feb 26 | ||||
| 7 | Feb 29 | Neural Networks Chapter 4: Artificial Neural Networks from Machine Learning, by Tom Mitchell
| Lab 5: Neural Networks | |
Mar 02 | ||||
Mar 04 | ||||
Mar 07 | Spring Break | |||
Mar 09 | ||||
Mar 11 | ||||
| 8 | Mar 14 | Supervised Learning
| Lab 6: Classification | |
Mar 16 | ||||
Mar 18 | ||||
| 9 | Mar 21 | Reinforcement Learning | Lab 7: Reinforcement Learning | |
Mar 23 | ||||
Mar 25 | Last day to declare CR/NC | |||
| 10 | Mar 28 | Reinforcement Learning
| Lab 8: MCTS Revisited | |
Mar 30 | ||||
Apr 01 | ||||
| 11 | Apr 04 | Unsupervised Learning
| Lab 9: Unsupervised Learning | |
Apr 06 | ||||
Apr 08 | ||||
| 12 | Apr 11 | Review for Exam 2 | Embodiment
| Exam 2 in lab |
Apr 13 | ||||
Apr 15 | ||||
| 13 | Apr 18 | Philosophy and Ethics of AI | Lab 10: Machine Learning Project | |
Apr 20 | ||||
Apr 22 | ||||
| 14 | Apr 25 | Deep Learning Andrey Kurenkov's A brief history of neural nets and deep learningAI in pop culture
| ||
Apr 27 | ||||
Apr 29 | ||||