Description

Instructor:
Bruce Draper
Office: 442 CS Building
Office Hours: By arrangement
Email: draper@colostate.edu
GTA:
Dejan Markovikj
Office:
Office Hours: Monday 8-10am, Wednesday 10-11am and Wednesday 7-8pm
Email: cs540@cs.colostate.edu
Lecture Time and Place:
12:30-1:45, Tue, Thur, CSB Room 425

Artificially intelligent agents perceive the world, solve problems by combining data, knowledge, and judgement, and act in the world. Examples of AI agents range from web search engines to scheduling agents to humanoid robots. This course will cover representations and algorithms in several core subareas of artificial intelligence, beginning with advanced search and planning. We will then tackle more advanced topics selected in response to student interests. Candidate topics include (but are not limited to): evolutionary computation and particle filters, ensemble learning, Bayesian networks, Markov and other time-series models, data mining, information retrieval, and natural language interpretation/generation.

Course Requirements

The class is structured around a lecture format and team projects. Class discussions, questions and participation (in class or via online discussions) are strongly encouraged (not to mention 20% of your grade, as discussed below).

Pre-requisites:

CS440 or equivalent. Knowledge of fundamentals of Artificial Intelligence, search and logic. Roughly the material covered in the first ten chapters of Russell & Norvig.

Grading:

The course requires class participation and projects. The project topics are assigned in very broad terms. The teams are expected to do interesting work within the assigned parameters, and then write a paper and give a short oral presentation in which they justify their specific approach, compare it to the reading assignments, and tell us what they have learned. Every project particpant must also write a paragraph outlining their contributions to the project, and not every member of a team will necessarily receive the same grade for a project. There will be two or three projects (depending on class size), and the projects are allowed to build on each other (in fact, its encouraged). Grades will be determined as follows:

Activity Weight
Participation (in class or via online discussions) 20%
Projects 80%

Materials

To train students to read the literature, reading assignments will be journal and conference papers, the links to which will be posted online. The written portion of class projects are expected to including the reading assignments in their literature reviews, and compare and contrast the paper with their projects.

Late Policy:

Projects are multi-week, multi-person assignments. There is no excuse for being late, and late projects will not be accepted.
If something radical happens in your life that makes it impossible to make the deadline (e.g., a family member is rushed to intensive care and you need to be there or better yet you win mega-millions and have to be holed up with lawyers for the next 4 days), contact the instructor ASAP. However, the assignments are expected to take some time, so the instructor will have little sympathy if you had not yet started it and you are within 48 hours of the deadline.

Class Participation

All students taking this course are expected to participate actively. For all students, includes asking and responding to questions. For distance students, the mechanism for asking and responding to questions is Piazza. How much you interact will factor into the Participation grade for the class.

Students registered in the on-line section will be able to watch video recordings of lectures. These will be available through Canvas.

Office Hours:

Generally, I have found that graduate students prefer arranging times at our mutual convenience. However, for the DL students, I can set a time each week that I will be available through the chat facility on RamCT. In any case, if you want to talk outside that time, send an email to arrange a time.