Class: | Mon., Wed., Fri. 10:30 - 11:20 PM, SCI 105 |
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Lab: | Fri. 2:15 - 3:45 PM, SCI 256 |
Professor: | Lisa Meeden |
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Email: | meeden at cs dot swarthmore.edu |
Office: | Science Center 243 |
Office Hours: | Tue., Wed. 1:30-3:30 |
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, simulated annealing, and genetic algorithms 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 10/11/19 |
20% | Exam 2, in lab 11/22/19 |
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 to 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.
You will clone a reading journal repo containing several weekly text files. 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 Friday, during the scheduled lab time, and will be due by the following Thursday before midnight.
You should work with a partner on all labs after lab 0. We will be using Teammaker to facilitate the creation of partnerships. 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. You should review the College's medical excuse policy.
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 | Sep 02 | Introduction to AI
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Sep 04 | ||||
Sep 06 | ||||
2 | Sep 09 | Problem solving with search
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Sep 11 | ||||
Sep 13 | ||||
3 | Sep 16 | Local search
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Sep 18 | ||||
Sep 20 | ||||
4 | Sep 23 | Game tree search
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Sep 25 | ||||
Sep 27 | ||||
5 | Sep 30 | Monte Carlo Search
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Oct 02 | ||||
Oct 04 | ||||
6 | Oct 07 | Evaluating Classical AI
| EXAM 1, Friday in lab | |
Oct 09 | ||||
Oct 11 | ||||
Oct 14 | Fall Break | |||
Oct 16 | ||||
Oct 18 | ||||
7 | Oct 21 | Perceptrons and Neural Networks
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Oct 23 | ||||
Oct 25 | ||||
8 | Oct 28 | Deep Learning
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Oct 30 | ||||
Nov 01 | ||||
9 | Nov 04 | Reinforcement Learning
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Nov 06 | ||||
Nov 08 | ||||
10 | Nov 11 | Genetic Algorithms
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Nov 13 | ||||
Nov 15 | ||||
11 | Nov 18 | Evaluating Machine Learning
| EXAM 2, Friday in lab | |
Nov 20 | ||||
Nov 22 | ||||
12 | Nov 25 | Final Project | No lab | |
Nov 27 | No class | |||
Nov 29 | Thanksgiving | |||
13 | Dec 02 | Melanie Mitchell visiting | Philosophy of AI
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Dec 04 | ||||
Dec 06 | ||||
14 | Dec 09 | Future of AI
| Finish Project |