Course Syllabus

Class Policies
CSCI B351/COGS Q351: Intro to Artificial Intelligence

(This information is subject to change)

Staff

All email addresses are <username>@indiana.edu

Professor

Gregory Rawlins

Office: Lindley Hall 201F

Username: rawlins

Office hours: By appointment

Associate instructors: 

Paul Kusisto

Office: Lindley Hall Pit ("The Abyss")

Username: pkusisto

Office Hours: Thursday, 4:00 PM to 5:00 PM, and by appointment

 

Pranav Pande

Office: LH 004

Username: pmpande

Email: pmpande@iu.edu

Office Hours: Wednesday, 4:00 PM to 5:00 PM

 

Rohit Patil

Office: Info East Lobby

Username: rnpatil

Office Hours: Wednesday, 1:00 PM TO 2:00 PM

 

Jay Nagle

Office: Info East Lobby

Username: jaynagle

Office Hours: Thursday,  1:00 PM to 2:00 PM

 

Prerequisites

This course is aimed at undergraduate students in Computer Science, Cognitive Science, or related fields who have a computing background.

Experience with computer programming, data structures, and algorithms will be assumed. Programming projects shall be written in Python. Python has a reputation for being extremely easy to learn; those with a strong programming background can develop competency in Python in hours or days.

Textbook

Russell & Norvig's Artificial Intelligence: A Modern Approach, Second edition, Prentice Hall, 2002.

Meeting times

Classes meet on 5:45PM-7:00PM on Mondays and Wednesdays in Informatics East (I2) 130.

Syllabus

* Search
Blind Search	Breadth-First Search
	Depth-First Search
	Iterative Deepening
	Uniform Cost
Informed Search	Greedy
	A*
Adversarial Search	MiniMax
	Alpha Beta
* Logic	Propositional Logic
	Predicate Logic
	Expert Systems

Midterm Exam Dates

Midterm Exam 1: 17th October 2016

Midterm Exam 2: 16th November 2016

Final Exam Date

TBD (Project final strongly encouraged)

Policies

Everyone is responsible for reading the departmental statement on academic integrity before starting the first assignment.

Homework assignments may be completed in pairs, but only if the partner’s name and username are clearly indicated in the submission.

Homework assignments are due by the end of class on the due date unless otherwise specified. Extensions are granted only in extenuating circumstances at the instructor’s discretion. Requests for extensions require advance notice (except for emergencies).

Late homework will be docked 20% for each 24 hour period after the due date, up to 80% off.

Canvas Discussion, and Instructor Questions

Questions can be directed to the canvas discussion section.  Please allow sufficient time for responses before assignment deadlines.

If you have questions regarding grades for assignments, quizzes, exams, etc., please ask such questions in person to one of the TAs before/after class or by scheduling a time to meet with one of the TAs. We will not answer questions regarding grades over email. 

Grading policy

10%	5 quizzes (in class)
30%	5 assignments
20% + 20%	2 exams
20%	One of: ·         Expert interview + paper + presentation Paper should be professionally done, 4 pages or longer, with references. Presentation should be clear and enlightening Topics TBD ·         Programming Project Working code should demonstrate something interesting with AI components ·         Evil written final (comprehensive material) - least desired For team work, performance will be evaluated by comparing individual and group performance and by evals to explain what you did.

The project will be due sometime during the last few weeks of the semester, so you will have plenty of time to create something really meaningful.

Project Requirements
Expert Interview: 2-4 people teams (we need exactly 1 team for each professor).

• Research topic prior to the interview.
• Meet with your chosen professor (you must only meet once) for an interview.
  • Interviews can last up to an hour - arrange a time with your individual professor.
• Write a 4+ page written report (single spaced)
  • This paper is the main deliverable for the expert interviews, so it must be extremely high quality; it should intensely edited and revised during the course of the project.
  • The paper must also include relevant references, and citations from both the extensive research performed before the interview, as well as information gathered during the interview itself.
• Give a presentation to the class about the topic, including both your research and the insight you gained from the interview.

Programming Project: 2-3 person teams

• Design working code to work to solve/improve/explore some problem of interest related to AI.
• Your project should demonstrate something INTERESTING about AI components.
• Write a short paper explaining your project, and your design, from a technical standpoint.
• Present your project/product to the class.  The nature of this presentation is very dependent upon your project, but should include:
  • An overview of your problem, and your design(s)
  • A technical discussion of the AI components used
  • Problems encountered, and what your team would do differently next time
  • RESULTS!  What did your team accomplish?
  • Above all, these presentations should excite and engage us in your project!
• The main goal is to try to connect each student to something which they are interested in and encourage exploration of all these topics which fall within AI. Since this is an introductory course and there are so many topics it would be impossible to teach all of them, but having everyone learn about one specific topic and then share it with the class is much more plausible.

Feel free to reach out to us if there are any questions, as always.