CIS3203 Introduction to Artificial Intelligence

CIS9603 Artificial Intelligence

Fall 2011

Schedule

Instructor

• Longin Jan Latecki, Professor, email: latecki@temple.edu
• Office: 306 Wachman Hall, phone: 215-204-5781
• Office Hours: Wednesday 12:00-1:00 PM and Thursday 10:00-11:00 AM or by appointment

TA for CIS3203

• Nemanja Djuric, email: tuc17157@temple.edu
• Office: Wachman Hall 319
• Office Hours: Monday 1:30-3:00 PM

Class

• Meets: Wednesday 2:00-3:10 PM  in Wachman 104 and Thursday 8:00-9:20 AM in TL 1B

Recitation

• Meets: Wednesday 3:10-5:00 PM  in Wachman 104

Prerequisites: Minimum Grade C in the following courses:

• CIS2166 or Math3098 or equivalent, and
• CIS2168 or equivalent, and
• Understanding of probability and statistics on the level of CIS3033 or equivalent
• A solid understanding of probability and linear algebra, you should be familiar with most of the material in the two links: probability and linear algebra

Textbooks (Most of the course is based on the first one):

David Poole and Alan Mackworth, Artificial Intelligence. Foundations of Computational Agents. Cambridge University Press 2010 website

Stuart Russell and Peter Norvig. Artificial Intelligence. A modern Approach. Third Edition. Prentice Hall, 2010.

Stephen Marsland, Machine Learning: An Algorithmic Perspective  CRC 2009.
Book's website: http://www-ist.massey.ac.nz/smarsland/MLBook.html

Topics

Since today's AI is mostly learning and inference, we will focus on both subjects.
This course is designed as an advanced undergraduate and introductory graduate course in learning and inference.
It introduces the basic concepts by focusing on their intuitive understanding and algorithmic perspective.
It is intended to prepare the students for future graduate courses in machine learning, data mining, robotics, and computer vision.
Part of the course will be based on the following online course:

More info is on http://www.ai-class.com/

The online schedule and the list of topics is on http://robots.stanford.edu/cs221/online.html

The students are strongly encouraged to attend the online lectures for four weeks starting on October 10.

During this time the online lectures will be reviewed and discussed during our lectures.

Exams, Project and Grading

• Homework: 10%
• Class participation: 10%
• Midterm and Final Exams: 60%
• Quizzes: 20%

  Homework: Homework due dates are on Thursday. Late homework will result in a penalty. You will be allowed to drop your lowest three homework grades. 

  Class attendance: Class attendance is expected, and may be recorded from time to time. Absences for legitimate professional activities and illnesses are acceptable only if prior notice is given to the instructor by e-mail or phone. Scheduling conflicts with your work, extra-curricular activities, or any other such activities is not a valid excuse. Although attendance is not a specific part of the course evaluation it has a direct effect on class participation. If you are not in class you cannot participate. Class participation means that you attend class regularly and have completed your assigned readings. It means that you ask relevant questions and make informed comments in class. Class participation will contribute to the final grade.

  Exams: If you miss a midterm for an emergency [as agreed ahead of time with the instructor], there will be no makeup exam: the other exams will become proportionally more important. If you miss any exam without prior agreement, and without definitive proof as to the reasons, you will get a zero.

Honor Code

• All work submitted for credit must be your own.
• You may discuss the homework problems with your classmates, the teaching assistant, and the instructor. You must acknowledge the people with whom you discussed your work, and you must write up your own solutions and code. Any written sources (apart from the text) used must also be acknowledged; however, you may not consult any solutions from previous years' assignments whether they are student or faculty generated.
• Plagiarism will be handled with severe measures.
• Please ask if you have any questions about the Honor Code. Violations of the honor code will be treated seriously. Please check the Temple University policy on Plagiarism and Academic Cheating.

Disabilities

I encourage students with disabilities, including "invisible" disabilities such as chronic diseases and learning disabilities, to discuss with us any appropriate accommodations that we might make on their behalf. Student must provide me with a note from the office of Disability Resources and Services at in 100 Ritter Annex, 215-204-1280, regarding their disability.