Final projects are now due Sunday, May 5th by midnight.
Class: | Mon., Wed., Fri. 11:30 - 12:20PM, Science Center 181 |
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Lab A: | Thursday 1:05 - 2:35PM, Clothier |
Lab B: | Thursday 2:45 - 4:15PM, Clothier |
Professor: | Lisa Meeden |
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Email: | meeden at cs dot swarthmore.edu |
Office: | Science Center 243 |
Office Hours: | Tuesday 1:30-3:30pm Wednesday 1:30-3:30pm And any time my door is open |
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.
5% | Class Participation |
10% | Reading Journal |
35% | Labs |
20% | Exam 1, in lab 2/28/19 |
20% | Exam 2, in lab 4/18/19 |
10% | Final Project |
Rather than using a single textbook, we will be using materials from a variety of sources including:
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.
You should check the class schedule and read any material that has been assigned before coming to class. You will get the most out of the reading if you approach it as follows:
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 11:59pm the night before class where they will be discussed. No late responses will be accepted.
In our first lab you will clone a reading journal repo containing several text files: one for the search readings, another for the machine learning readings, and a final one for the more philosophical issues we'll be reading about at the end of the semester. You will write your responses in the appropriate file and submit them via git (using add, commit, and push) before each class meeting when reading is assigned.
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:
Labs will be assigned on Thursday, during the scheduled lab time, and will be due by the following Wednesday 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 you submit your lab.
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 (e.g., extended illness, concussion, hospitalization) or other emergency, you must contact the dean's office and your instructors. Faculty will coordinate with the deans to determine and provide the appropriate accommodations. Note that for illnesses, the College's medical excuse policy, states that you must be seen and diagnosed by the Worth Health Center if you would like them to contact your class dean with corroborating medical information.
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.
If you believe that you need accommodations for a disability, please contact the Office of Student Disability Services (Parrish 113W) or 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.
WEEK | DAY | ANNOUNCEMENTS | TOPIC & READING | LABS |
1 | Jan 21 | No class | Introduction to AI
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Jan 23 | ||||
Jan 25 | ||||
2 | Jan 28 | State space search
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Jan 30 | ||||
Feb 01 | ||||
3 | Feb 04 | Local search
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Feb 06 | ||||
Feb 08 | ||||
4 | Feb 11 | Game tree search
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Feb 13 | ||||
Feb 15 | ||||
5 | Feb 18 | Monte Carlo Search
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Feb 20 | ||||
Feb 22 | ||||
6 | Feb 25 | Review Search Approaches | EXAM 1 | |
Feb 27 | ||||
Mar 01 | No class | |||
7 | Mar 04 | Markov Decision Processes
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Mar 06 | ||||
Mar 08 | ||||
Mar 11 | Spring Break | |||
Mar 13 | ||||
Mar 15 | ||||
8 | Mar 18 | Reinforcement Learning
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Mar 20 | ||||
Mar 22 | ||||
9 | Mar 25 | Perceptrons and Neural Networks
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Mar 27 | ||||
Mar 29 | ||||
10 | Apr 01 | Deep Learning
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Apr 03 | ||||
Apr 05 | ||||
11 | Apr 08 | Evaluating what Machines have Learned
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Apr 10 | ||||
Apr 12 | ||||
12 | Apr 15 | Review Machine Learning | EXAM 2 | |
Apr 17 | ||||
Apr 19 | ||||
13 | Apr 22 | Philosophy of AI
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Apr 24 | ||||
Apr 26 | Bias in AI and ML | |||
14 | Apr 29 | Future of AI
| Finish Project | |
May 01 | ||||
May 03 |