CS 371 - Introduction to Artificial Intelligence Course Information

Course Overview

A most fascinating research field, I like to describe Artificial Intelligence (AI) as the interesting "miscellaneous pile" of computer science.  For example, more expressive, computationally-complex database research finds its home in AI as "knowledge representation and reasoning".  The difficult inverse of computer graphics is machine vision.  Where do you think the object-oriented paradigm reached its maturity?  Many interesting, cutting-edge problems and developments find their birthplace and first home in this ambitious and visionary research community.

Presented with the unifying theme of constructing intelligent software agents, we survey an interesting variety of research problems and modern problem solving techniques.  Topics we cover may include search (uninformed, informed, stochastic local, game-tree), knowledge representation and reasoning (esp. reasoning under uncertainty), machine learning (reinforcement, supervised, unsupervised), applications and societal impact, and robotics.

Learning Objectives

• Understand the role of AI in the context of Computer Science, and how the diverse problem solving techniques can be unified in terms of agents and optimization.
• Experientially learn several AI problem solving techniques through pair programming work on challenge problems.
• Grow in the ability to observe complex problems, model them simply and formally through abstraction and approximation, and apply programming skill to create "intelligent" software artifacts.

Text

Stuart Russell, Peter Norvig Artificial Intelligence: A Modern Approach, 4th ed. Prentice Hall; ISBN-10: 0134610997, ISBN-13: 978-0134610993

Instructor

Grading

You are responsible to know the material from each lecture and reading assignment before the start of the next class.  Homework is due at the beginning of lecture on the due date.  Late work will not necessarily be accepted (exception: Late Assignment Policy below).  Code must be a legal program in the relevant language in order to be graded.  This includes compiling correctly with test code as indicated from submission feedback. (It need not be free from logic errors.) 

You are required to attend 2 colloquia or approved departmental events over the course of the semester.

Late Assignment Policy

It is your responsibility to notify me of your submission of late work.  Late homework will receive a flat 10% penalty (e.g. what would have received 95% would receive 85% credit, not 85.5%).  Late homework will only be accepted until either 6 classes later or half-way between the due date and the last day of classes (rounded down), whichever comes first.  For example, homework due at the beginning of class 4 would be accepted late until class 10, but homework due at the beginning of class 25 would be last accepted late at the beginning of class floor((25 + 28)/2) = floor(26.5) = 26.

Warning: Treat the late policy as a seldom-used pressure release to cope with unusual situations. If you chronically make use of the policy, (1) you'll be starting assignments far after the relevant material, increasing their difficulty, and (2) exams and pre/in-class exercises will presume that you're up-to-date and allow no extensions. Also, you need to communicate your desire to exercise the policy to me via email. In such cases, I recommend meeting with me in office hours to make a catch-up plan.

Attendance

Class attendance and participation is required.  If you attend all classes and are willing to participate, you'll get 100% for this part of your grade.  Even if you know enough to give a particular lecture, please consider the value of helping your peers during in-class exercises.

Woody Allen is quoted as saying "80% of success is just showing up."   While our class attendance/participation grade is not 80% of the final grade, it is critical that late arrivals (5 or more minutes after the beginning of class) and unexcused absences are not excessive.  Missing more than half of class unexcused is considered being absent.  An unexcused late arrival is counted as a half absence.  If the total number of absences counted this way exceeds 20% of class meetings, i.e. 6 absences or more, the student will have failed the course.

Work Expectations

You are expected to work an average of 9 hours per week beyond class time.  Gettysburg College policy, in accordance with federal and state standards, equates 1 credit unit with an average of 12 hours of work per week with 50 minute classes counting as 1 full hour of work.  During these remaining 9 hours beyond class, a student is expected to learn from assigned readings, complete exercises related to such readings, attend required colloquia, and complete assignments.

Think of your college studies as a more-than-full-time job, and engage in it with passion.  After all, you get out of it what you put into it, and it is my hope that you'll gain much from your investment in this course.  If you'd like to learn more about how to better track tasks and manage time as a student, consider watching my short tutorial on getting things done.

Honor Code

What is permitted:

• AI-generated code where (1) you include your prompts in comments, and (2) you have thoroughly understood, can explain, and have tested such code. Required reading to abide by: Prof. Presser's Generative AI Guidelines
• Discussing algorithms, syntax, and problem specifications with other students
• Seeking assistance (as needed) from CS professors, students assistants, and approved tutors
• Use of "starter code" supplied by your professor, or code you have previously developed

What is not permitted:

• AI-generated code where (1) you have not included your prompts in comments, or (2) you do not understand, cannot explain, or have not tested such code.
• Sharing code: Verbal dictation of code, transference of code (e.g. e-mail, file transfer, entry of printed output, etc.).  An important component of this experiential learning process is developing the ability to independently devise algorithms and express them in program code.
• Viewing and/or using unauthorized code (e.g. solutions found online, downloads from code repositories, etc.)
• Submitting work that implicitly takes credit for work that should be credited to others
• Efforts to obscure the source of unauthorized work
• Outsourcing work

Put simply, students may discuss assignments at an abstract level (e.g. specifications, algorithm pseudocode), but must actually implement solutions independently or in permitted groups.  Credit should be given where credit is due.  Let your conscience be your guide.  Do not merely focus on the result; learn from the process.