Artificial Intelligence, fall 2008

General information

Artificial Intelligence - Syllabus Fall 2008

Course schedule
Theory, A5 202, Tu 15-17, Luigi Ceccaroni
Theory, A5 202, Th 19-20, Luigi Ceccaroni
Recitations, A5 202, Fr 15-17, Núria Castell
Laboratory, B5 S101, Th 20-21, Luigi Ceccaroni

Course description
This course introduces representations, techniques and architectures used to build applied systems and to account for intelligence from a computational point of view. This course also explores applications of rule chaining, heuristic search, logic, constraint propagation, constrained search and other problem-solving paradigms. In addition, it covers applications of decision trees, knowledge representation, knowledge-based systems and natural-language processing. This course is in the first semester of the Master's degree in Information Technology (MTI). It accounts for 7.2 credits of work load, distributed as 3.6 credits for theory, 2.4 for recitations and 1.2 for laboratory.

Email usage guidelines
* Students must have individual email accounts. The first assignment is for students to use their own email accounts to send the instructor a message containing their full name and contact information by the second week of the semester. All messages have to contain the full name.
* Students must check their email messages at least once a day—The instructor sends online quizzes to encourage this.
* Students must turn in their completed assignments by email unless the instructor indicates otherwise.
* Students receive part of their semester grades on their conformity to usage guidelines, responsiveness, and content richness of their email communications.
* Students' messages must contain sender and message identification information in the subject field. For instance, a message may show the tags [MTI-AI] and [D02] in its subject field. MTI-AI would represent course Artificial Intelligence of the Master's degree in Information Technology and D002 would represent deliverable 02. If the subject field showed [MTI-AI, Q], the Q would represent a question about that course. Additional symbols may also be used, depending on the needs of the course. Because a few students will ignore or forget these rules at the beginning, instructors will enforce the rules firmly and return nonconforming messages to their senders.

Topics and lecture notes

    * Introduction [ppt]
    * Problem-solving
          o Solving problems by searching [ppt]
          o Informed search and exploration [ppt]
              Recommended reading: Mother Nature on a Motherboard by Jessie Scanlon
          o Adversarial search [ppt]
          o Constraint satisfaction problems [ppt]
    * Knowledge and reasoning
          o Introduction to knowledge representation [ppt]
          o Inference in first-order logic [ppt]
          o Ontological engineering [ppt]
          o Knowledge-based systems [pptx]
          o Knowledge engineering [ppt]
          o Uncertainty [ppt]
          o Probabilistic reasoning [ppt]
          o Fuzzy logic [ppt]
    * Natural language processing
          o Communication by natural language [ppt]
          o The different levels of language analysis [ppt]
          o Definite clause grammars and semantic interpretation [ppt]
          o Machine learning [pptx]