Course Syllabi

Instructional Materials to Help Plan a Whole Course

AITopics > Resources > Resources for Educators > Course Syllabi

Course materials for instructors of AI classes, organized by topic.

Introductory AI Classes

Stanford AI Class taught by Sebastian Thrun and Peter Norvig. Fall, 2011. Free. While this class is being offered online, it is also taught at Stanford University, where it continues to be a popular intro-level class on AI. For the online version, the instructors aim to offer identical materials, assignments, and exams, and to use the same grading criteria. Both instructors will be available for online discussions. Syllabus and information available.

Syllabus for Intro to AI Class Prof. Raymond Mooney, University of Texas, 2009-10. "The course is a basic introduction to artificial intelligence covering fundamental material in problem solving, heuristic search, knowledge representation, deduction, planning, uncertain reasoning, learning, and natural-language processing." Many links to lecture slides, homework, readings, etc.

Syllabus for Intro to AI ICS-271, Univ. of California, Irvine. Dr. Rena Dechter. Fall, 2008. With PowePoint slides.

Syllabus for AI- I Class Dr S.B. Holden, University of Cambridge, 2009-10.

Syllabus for AI - II Class Dr S.B. Holden, University of Cambridge, 2009-10.

Syllabus and Lecture Slides for Graduate AI Class -- 15-780 Graduate Artificial Intelligence Spring 2011; Geoff Gordon and Tuomas Sandholm; School of Computer Science, Carnegie Mellon University. Includes lecture slides.

Artificial Intelligence: Four video lectures by Patrick Winston,

ArsDigita University. "A quick overview of AI from both the technical and the philosophical points of view. Topics discussed include search, A*, Knowledge Representation, Neural Nets."

Carnegie Mellon University - Open Learning Initiative

Agents

Agent Curricula Database. "The development of this database is part of AgentLink's effort to highlight and improve available resources on Agent training and teaching in Europe."

Artificial Intelligence Course at Jaypee Institute of Engineering and Technology (JIET), Guna, by Prof. RC Chakraborty, Visiting Professor.
Course lectures, hours 42. There are 11 pdf files, total 562 pages. Topics : Introduction to AI; Problem Solving - Search and Control Strategies; Knowledge Representation Issues - Predicate Logic, Rules; Reasoning System - Symbolic, Statistical; Game Playing; Learning; Expert System; Fundamentals of Neural Networks; Fundamentals of Genetic Algorithms; Natural Language Processing; Common Sense.
"The Course on AI refers to the even semester (Jan–May) course, title: Artificial Intelligence, Code 07B61CI3-0-2, 4 Credits, Lectures-42 hours. This course was offered to the B.Tech. students of 6th semester in the previous years, 2006, 2007, 2008, 2009 and now in 2010 . The lecture slides, 565 numbers in pdf format, have gone through four updates. The course is at the Dept. of Computer Science & Engineering, Jaypee Institute of Engineering and Technology (JIET), Guna. This course is offered by Prof. RC Chakraborty, Visiting Professor at JIET. "

Course on Soft Computing at Jaypee Institute of Engineering and Technology (JIET), Guna, by Prof. RC Chakraborty, Visiting Professor.
Soft Computing : Course lectures, hours 42. There are 9 pdf files, total 398 pages. Topics : Introduction to Soft Computing; Fundamentals of Neural Network; Back Propagation Network; Associative Memory; Adaptive Resonance Theory; Fuzzy Set Theory; Fuzzy Systems; Fundamentals of Genetic Algorithms; Hybrid Systems.
" The Course on Soft Computing refers to the odd semester (July–Nov) course, title Soft Computing, Code 07B71CI4-0-8, 4 Credits, Lectures – 42 hours. This course was offered to the B.Tech. students of 7th semester in the previous years, 2007, 2008, 2009 and now in 2010 . The lecture slides, 398 numbers in pdf format, have gone through three updates. This course is at the Dept. of Computer Science & Engineering, Jaypee Institute of Engineering and Technology (JIET), Guna. This course is offered by Prof. RC Chakraborty, Visiting Professor at JIET."

Machine Learning

Machine Learning. Stanford CS229 graduate class in machine learning. Syllabus, Course Information, Projects.

Natural Language Processing

Empirical Methods in Natural Language Processing. CSCI 562, University of Southern California, Fall 2011. "This course provides a systematic introduction to statistical models of human language, with particular attention to the structures of human language that inform them and the structured learning and inference algorithms that drive them."

Robotics

The Robotics Academy at the National Robotics Engineering Center, Carnegie Mellon University. Resources include Robotics Engineering Curricula for middle school and high school classrooms.

RoboEducators Robotics Curriculum. This is an organized collection material that is related to Science, Engineering and Technology. It is intended to be used in 9th grade science classes, roobotics classes and robotics clubs. This material could be used as resources to augment a textbook or could be used without a textbook.

Robotics Curriculum from BEST, Robotics Inc. "Dr. Michael Wienen, Ph.D., of Brazos BEST has developed a new robotics curriculum that he is currently introducing not only to participating BEST teachers, but to any teacher who is interested in incorporating robotics into Physics/Science or Pre-Engineering curricula in middle, junior, or high schools." His EST [Engineering, Science & Technology] Foundations curriculum includes a section about Automation, Robotics, and Society.

Robots and Robotics in Undergraduate AI Education. AI Magazine 27(1), Spring 2006:

• Components, Curriculum, and Community: Robots and Robotics in Undergraduate AI Education. By Zachary Dodds, Lloyd Greenwald, Ayanna Howard, Sheila Tejada, and Jerry Weinberg. AI Magazine 27 (1): Spring 2006, 11 - 22. "This editorial introduction presents an overview of the robotic resources available to AI educators and provides context for the articles in this special issue. We set the stage by addressing the trade-offs among a number of established and emerging hardware and software platforms, curricular topics, and robot contests used to motivate and teach undergraduate AI."
• Unifying Undergraduate Artificial Intelligence Robotics: Layers of Abstraction Over Two Channels. By Frederick L. Crabbe. AI Magazine 27 (1): Spring 2006, 23 - 37.
• The Pyro Toolkit for AI and Robotics. By Douglas Blank, Deepak Kumar, Lisa Meeden, and Holly Yanco. AI Magazine 27 (1): Spring 2006, 39 - 50.
• Launching Into AI's October Sky with Robotics and Lisp. By Frank Klassner. AI Magazine 27 (1): Spring 2006, 51 - 65.
• CMRoboBits: Creating an Intelligent AIBO Robot. By Manuela M. Veloso, Paul E. Rybski, Scott Lenser, Sonia Chernova, and Douglas Vail. AI Magazine 27 (1): Spring 2006, 67 - 82.
• Using Educational Robotics to Motivate Complete AI Solutions. By Lloyd Greenwald, Donovan Artz, Yogi Mehta, and Babak Shirmohammadi. AI Magazine 27 (1): Spring 2006, 83 - 95.

The Educational Robotics Cyber Laboratory "is a constantly evolving curriculum site for teachers, students, and other users who are interested in investigating real-world problems in educational robotics. Many of these laboratory activities are especially designed for participants of the KISS Institute for Practical Robotics Botball program, however, anyone with a LEGO Mindstorm kit, LEGO Technic, or a HandyBoard processor can join in the fun. We encourage teachers to incorporate these science and technology enhancement activities into their school's curriculum. We are well aware that robots and robotics technology may be new to most teachers and their students. Thus, the content of the Cyber Laboratory covers the very basics of robot design and experimentation. More advanced activities and research projects are provided to challenge experienced robot designers."

Semantic Web

Semantic Web Technologies course 2009/2010. Taught by Jos de Bruijn and Maria Keet. KRDB Research Centre, Faculty of Computer Science, University of Bozen-Bolzano. "Aim: The aim of the course is to make the students familiar with the Semantic Web, with technologies used on the Semantic Web, and with applications using Semantic Web technologies. The course will focus on the theoretical background of various languages on the Semantic Web such as RDF, SPARQL, and OWL, and the practical use of these languages on the Semantic Web. In addition, the course will focus on ontology engineering and important application areas for Semantic Web technology, namely the Life Sciences."

Other Resources

University of Texas World Lecture Hall. Based at UT's Austin's Center for Instructional Technologies (a unit of the Division of Instructional Innovation and Assessment), "World Lecture Hall publishes links to pages created by faculty worldwide who are using the Web to deliver course materials in any language." Among several dozen computer science courses listed in the Computer Science section are many AI and AI-related courses.

OER [Open Educational Resources] Commons: "a teaching and learning network, from K-12 lesson plans to college courseware, from algebra to zoology, open to everyone to use and add to." Some links are broken, but search on "artificial intelligence". Courses, collections & libraries include: