CSC242: Intro to Artificial Intelligence
Spring 2016

General Information

Instructor: Prof. George Ferguson

Web: BlackBoard and


Office: CSB731

Office hours: Mon 1330-1430, Tue 1100-1200, Wed 1100-1200 (subject to change)

Lectures: Tue/Thu 9:40-10:55, Dewey 1-101

Teaching Assistants

TA Study Halls in CSB601: Mon 1150-1305, Thu 1525-1640, Thu 1900-2015


Course Goals

This course introduces fundamental principles and techniques from Artificial Intelligence, including heuristic search, automated reasoning, handling uncertainty, and machine learning. An important goal of the course is to prepare students for advanced AI courses.


CSC172 is required. CSC173 is strongly recommended (and may be required in future years).

Textbook & Resources

Artificial Intelligence: A Modern Approach, Russell & Norvig, 3rd ed.

This book is excellent and will be a worthwhile addition to your Computer Science bookshelf.

Additional readings and resources will be posted as necessary.


The course will follow the textbook very closely. The following outline describes the main sections of the course, specific topics therein, and specific sections from the textbook corresponding to each topic. Please see the Course Calendar for more details.

Search Problem Solving 3.0-3.3.1
Search Strategies 3.3.2-3.6
Local Search 4.0-4.1
Adversarial Search 5.0-5.2.2, 5.3-5.4.2
Stochastic and Partially Observable Games 5.5-5.6; 5.7-5.9 fyi
Representation Constraint Satisfaction 6.0-6.4
Propositional Logic 7.0-7.4
Propositional Inference 7.5
First-Order Logic 8.0-8.3; 8.1.1-8.1.2 fyi
First-Order Inference 9
Uncertainty Probabilistic Reasoning 13.0-13.2; 13.2.3 fyi
Probabilistic Inference 13.3-13.6
Bayesian Networks 14.0-14.2
Inference in Bayesian Networks 14.4-14.4.1, 14.5; 14.7 fyi
Uncertainty Over Time 15.0-15.3
Simple Decision Making 16.0-16.1; 16.2-16.3, 16.5 fyi
Complex Decision Making 17.0-17.1; 17.2-17.3, 17.5 fyi
Learning Learning From Examples 18.0-18.4
Learning Methods 18.6; 18.7-18.9 fyi
Learning Probabilistic Models 20.0-20.2.2; 20.2.5 fyi; 20.3-20.3.4
Reinforcement Learning 21.0-21.3; 21.5 fyi

Sections marked fyi are for your information—reading and understanding them will give you a better understanding of the topic, but their material will technically not be on quizzes and exams.

This list is subject to change at any time. Any changes will be announced in class.


The following elements contribute to your grade in this course:

Grade Weighting (subject to change):

Problem Sets~200%

Letter grades will follow the Official University of Rochester Grading Scheme. Note that the University scheme puts average somewhere between C and B. The following table is an estimate of how the numeric grades will map onto the letter grades (subject to change):

A: Excellent >=90%
B: Above Average >=75%
C: Minimum Satisfactory Grade >=60%
D: Minimum Passing Grade >=50%
E: Fail <50%

Requirements and Policies

It is important that you attend class (lecture), but it is not mandatory and we do not take attendance.

I do my best, but not everything can be presented in full detail during lecture. You should read the appropriate part of the textbook BEFORE class. You should read it again afterwards. You are responsible for the material whether or not it is presented in class.

There will be regular quizzes given during class. Quizzes may NOT be made up without suitable prearranged excuse. Missed quizzes receive a grade of ZERO.

Projects are larger assignments, typically taking about two weeks to complete. Late projects are assessed a 2% per hour penalty (so approximately 2 day maximum). No exceptions without a suitable prearranged excuse.

If you use your own computer, crashes, malfunctions, and catastrophic loss of files is NOT an excuse. Backup your files regularly to at least one external drive and/or cloud storage. You can always complete your assignments using the lab and IT Center computers.

Students with an appropriate excuse for missing a quiz, project, or other deadline must make arrangements IN ADVANCE. Do not leave this until the last minute.

Students with an accommodation for any aspect of the course must make arrangements through the Center for Excellence in Teaching and Learning (CETL) IN ADVANCE. Then, as instructed by CETL, contact the instructor to confirm your arrangements. Do not leave this until the last minute either.

Students who are unable to attend or complete any part of the course due to illness should contact the instructor AS SOON AS POSSIBLE. Please note that the University Health Service (UHS) does not provide retroactive excuses for missed classes. Students who are seen at UHS for an illness or injury can ask for documentation that verifies the date of their visit(s) to UHS without mention of the reason for the visit. Students with extended or severe illness should contact the College Center for Advising Services (CCAS) for advice and assistance.

Academic Honesty

All assignments and activities associated with this course must be performed in accordance with the University of Rochester's Academic Honesty Policy. More information is available at:

All work you do in this course must be your own. Collaboration on projects is permitted, subject to the following requirements:

You must be able to explain anything you submit in person at any time.

Policy on Electronics

Please turn off or put into airplane mode all phones and leave them out of sight.

Unless you are taking notes with a laptop, you do not need it.

Even if you think you want to take notes with a laptop, you may be interested to know that research shows that students who take notes using pen and paper retain significantly more of the information. Typing your handwritten notes into the computer after class improves understanding even more.

If you insist on using your laptop to take notes, please turn off your wifi and otherwise disable notifications. You should probably also sit at the back of the room to avoid distracting the people sitting behind you.

Please note Section V.7 of the College's Academic Honesty policy regarding Unauthorized Recording, Distribution or Publication of Course-Related Materials.