CSCI 335 - Artificial Intelligence

Spring 2020

Official Pandemic Revision

Course Overview:
This course is an introduction to understanding and implementing computer systems that fall under the heading of "artificial intelligence". At the end of this course, you will be expected to be able to:

Develop software that exhibits some degree of intelligent behavior relative to a given domain
Understand and apply heuristic search
Determine whether a heuristic function is monotonic
Apply an adversarial search algorithm in the context of a computer game
Understand the basics of automated symbolic planning
Understand and apply the following machine learning algorithms
- Multi-layer perceptron (including autoencoders)
- Self-Organizing Map
- k-nearest-neighbor
- Naive Bayesian Classifier
- Decision tree learning (including random forests)
- Q-learning
Distinguish among supervised learning, reinforcement learning, and unsupervised learning
Employ scientific experiments to analyze the performance of an intelligent system
Assess the scientific basis for claims about artificial intelligence

Instructor: Dr. Gabriel Ferrer

Office Hours (M.C. Reynolds 312):
By appointment. To make an appointment with me, visit http://drferrer.youcanbook.me. From there, you can see my availability and select an appointment time.

Class Web Page: http://ozark.hendrix.edu/~ferrer/courses/335/s20/

Lecture Time: B4 (2:45 - 4:00 pm, Tuesday/Thursday)

Final Exam Period: Thursday, May 7, 8:30-11:30 am

Required Textbook: None

Suggested Textbook: Artificial Intelligence: A Modern Approach, 3rd Edition, by Stuart Russell and Peter Norvig

Grading:
There are a total of 1,000 points available over the course of the semester. The thresholds for earning each letter grade are as follows:

Letter grade	Points to achieve
A	900
B	800
C	700
D	600

Here are the semester's assignments and the associated points for each:

Assignment	Total Value
Project 1	85
Project 2	85
Project 3	85
Project 4	85
Project 5	85
Project 6	85
Project 7	85
Project 8	85
Project 9 Cancelled Everyone gets 85 extra points	85
Final Project	235

Programming Projects: You will develop your understanding of artificial intelligence by developing software that uses AI algorithms to achieve goals in a concrete domain. These assignments will use the Java programming language.

Each project (85 points total) will involve each of the following:

Programming assignment (25 points): Implement an AI algorithm for a given task.
Experimentation (20 points): Run a series of experiments using your implemented algorithm.
Progress report (5 points): Give a one-slide PowerPoint progress report during the class period prior to the project's due date. In this report, present the results of your experiments so far.
Paper (25): You will write a paper describing the results of your experiments.
Final presentation (10 points): Give a three-slide PowerPoint presentation describing the results from your paper.

Presentations: As mentioned above, for each programming project you will give two presentations: a progress report and a final report. These presentations will be graded for clarity of exposition, proper use of slides, and accuracy of content and analysis.

Revised Presentations: Presentations may be given three ways:

Live via videoconferencing
Prerecorded video
Submission of slides with no video

Revisions: After projects are returned, you are welcome to revise and resubmit your work. I will grade anew each submitted revision, and average the original and revised grades to produce a new grade for that assignment. Revisions may be submitted anytime until the start of the final exam period.

NEW: Revised Deadlines: Anytime prior to the deadline for a project, you may email me and inform me of a deadline that would work better for you. As long as it is prior to the last day of finals (May 13), your new suggested deadline will be acceptable.

If you do not notify me of a revised deadline, then no late work will be accepted. Any work not submitted on time is a zero. However, you may submit a solution after the deadline to qualify under the revision policy. In effect, this means that late work can earn up to half credit.

Final Project: Towards the end of the semester, you will select a final project topic. You will develop an intelligent system that either extends a concept that we have explored this semester, or uses a concept that we have not covered. You will give an oral presentation of your project during the final exam period for this course. The project write-up will also be due at that time.

Disabilities: It is the policy of Hendrix College to accommodate students with disabilities, pursuant to federal and state law. Any student who needs accommodation in relation to a recognized disability should inform the instructor at the beginning of the course. Students should contact Julie Brown in Academic Support Services (505-2954; brownj@hendrix.edu) to begin the accommodation process.

Schedule: The anticipated schedule for the semester is below. The instructor reserves the right to alter the schedule as necessary during the semester.

Date	Day	Topic/Activity	Assignment Due
1/21	Tuesday	Introduction Search Algorithms	None
1/23	Thursday	Search Algorithms	None
1/28	Tuesday	Presentations	Project 1
1/30	Thursday	Adversarial Search	None
2/4	Tuesday	Adversarial Search	None
2/6	Thursday	Presentations	Project 2
2/11	Tuesday	Machine Learning: kNN and Naive Bayes	None
2/13	Thursday	Machine Learning: kNN and Naive Bayes	None
2/18	Tuesday	Presentations	Project 3
2/20	Thursday	Neural Networks: Perceptrons	None
2/25	Tuesday	Neural Networks: Perceptrons	None
2/27	Thursday	Presentations	Project 4
3/3	Tuesday	Neural Networks: Self-Organizing Map	None
3/5	Thursday	Neural Networks: Self-Organizing Map	None
3/10	Tuesday	Presentations	Project 5
3/12	Thursday	Q-Learning	None
3/17	Tuesday	Pandemic	None
3/19	Thursday	Pandemic	None
3/24	Tuesday	Spring Break	None
3/26	Thursday	Spring Break	None
3/31	Tuesday	Q-Learning	None
3/2	Thursday	Presentations	Project 6
4/7	Tuesday	Machine Learning: Decision Trees and Random Forests	Final Project Proposal
4/9	Thursday	Machine Learning: Decision Trees and Random Forests	None
4/14	Tuesday	Open question day	Revised Final Project Proposal
4/16	Thursday	Presentations	Project 7
4/21	Tuesday	Automated Reasoning	None
4/23	Thursday	Automated Reasoning	None
4/28	Thursday	Presentations	Project 8
4/30	Thursday	Project work	Progress report
5/7	Thursday 8:30-11:30 am	Final Project Presentations	Final Project Report