Course Objectives: the student will understand

1. knowledge representations for AI systems
2. various forms of state-space search, including DFS, BFS, and heuristic search
3. biology-inspired methods of machine learning such as neural networks and genetic algorithms
4. layering of supervised and unsupervised learning methods to create deep learning networks

Textbooks.

• Artificial Intelligence: Foundations of Computational Agents, 3rd Ed. by David Poole and Alan Mackworth, Cambridge University Press - required
• Deep Learning with Python, 3rd Ed. (eBook) by François Chollet and Matthew Watson, Manning Publications - required

Course website:  https://scarl.sewanee.edu/CS356/

The course schedule, lecture notes, assignments, and this syllabus are all posted on the course website. Video lectures (if needed) and other material will be posted on BrightSpace.

Grading

Policies - Quick Look

• Attendance is an important factor in doing well in the class as there will be the occasion in-class exercise; the student is responsible for making up any work missed due to absence.
  The Dean's Office may be notified after three unexcused absences.
• Tutorial assignments are used to gain experience with a specific AI programming technique. Students are to work independently unless group work is specifically indicated. Assignments will typically be turned in using BrightSpace, though occasionally I may require a printed copy of program files.
• Late assignments are penalized 10% for each day late, but every student has 3 grace days for the semester, covering things like schedule crunches, road games, illnesses. Save these as long as possible. No assignment more than 4 days late will be accepted for grading.
• Lecture summaries: to support the intellectual life of the university, I encourage you to attend at least two lectures/talks outside of the formal classroom experience and submit a 1-2 page summary of the ideas presented, worth up to 4 points of extra credit.
• The Honor Code applies to all exams and assignments. Plagiarism (defined below) applies to any medium submitted, electronic or otherwise. See below for more information.

Course Administration:

The first part of this course will mainly consist of lectures and a few in-class exercises to become familiar with the concepts. The second part will follow more of a workshop model: short lectures (and possibly an occasional video lecture) followed by at least one in-class exercise or programming lab per week. One of the main ways you'll learn this material is by getting knee-deep into it yourselves.

Assignments

Assignments will typically involve interacting with an existing AI program written in Python, and modifying it in various ways. Later in the semester we'll be working almost exclusively with Jupyter Notebooks, experimenting with Deep Learning programs using the Keras API.
 

Avoiding plagiarism: Students may discuss ideas for solving an assignment among themselves: concepts and design issues, how to use software tools, how to fix compilation errors. However, each assignment must be your own work unless collaboration is specifically allowed. Turning in any portion of work written by another is an Honor Code violation and grounds for disciplinary action as allowed by University policy. You should not copy a file, supply a copy of a file, coach another student in writing code line by line, or look at another's code. A good rule of thumb: explaining how to fix a coding problem in English is fine, sitting down and fixing someone else's code is not. See below for the official Univerity policy.
 

For lecture summaries you'll attend and report on up to two lectures outside class, such as our department's Ebey lecture or lectures sponsored by other departments. You might also find useful technical presentations online, such as the Heidelberg Laureate series, the Strange Loop conference channel on YouTube, and other computing-related meetings for researchers or software developers.

A BrightSpace Explainer

Since the pandemic, BrightSpace (Sewanee's Learning Management System) has been the entry point into our courses.  I've posted a link to the course website, lecture videos, and space for submitting assignments.
 

Scheduling Office Hours

Students are welcome to meet me in my office during the office hours posted above; otherwise, schedule a time to meetup in my office or by Zoom. I use the Calendly app for scheduling; see the video on their website for a quick introduction to this service.
 

Honor Code Statement

When you matriculated, you agreed to follow the Honor Code: to do your own honest work and not to cheat in any form. All forms of cheating, including plagiarism, are violations of the Honor Code and will be treated as such. As per the Honor Code, plagiarism is defined as "[copying or imitating] the language and thoughts of others and [passing] the result off as an original work." Using the language or ideas of others without proper citation is considered academic dishonesty (cheating), and "others" includes responses from artificial intelligence processing programs (for example, ChatGPT). As well, using AI to complete assignments without the express and clear approval of your instructor is also a violation (for receiving unauthorized assistance). If you ever have a question about an assignment or need additional help, please ask for the instructor for assistance rather than jeopardize your academic career.
 

ADA Statement.

The University of the South is committed to fostering respect for the diversity of the University community and the individual rights of each member of that community. In this spirit, and in accordance with the provisions of Section 504 of the Rehabilitation Act of 1973 and the Americans with Disabilities Act (ADA), the University seeks to provide students with disabilities with the reasonable accommodations needed to ensure equitable access to the programs and activities of the University.

Any student with a documented disability needing academic adjustments is requested to speak with Student Accessibility Services (SAS) as early in the semester as possible, though students may reach out at any point during the academic year. If approved for accommodations, the student has the responsibility to present their instructors with a copy of the official letter of academic accommodations. Please note: Accommodation letters should be dated for the current term; accommodations will not be provided without a current accommodation letter; and accommodations cannot be applied retroactively.

SAS is located in the Office of the Dean of Students (931.598.1229). Additional information about accommodations can be found on the Student Accessibility Services website.