Course Overview
| Lectures | Tue. & Thu. 10:30 AM - 11:50 AM in Wean Hall 5409 |
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| IMPORTANT | Please periodically check the News page for important updates. |
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This course is targeted at graduate students who need to learn about
current-day research, and about how to perform current-day research,
in Artificial Intelligence---the discipline of designing intelligent
decision-making machines.
Techniques from Probability, Statistics, Economics, Algorithms,
Operations Research and Optimal Control are increasingly important
tools for improving the intelligence and autonomy of machines, whether
those machines are robots surveying Antarctica, schedulers moving
billions of dollars of inventory, spacecraft deciding which
experiments to perform, or vehicles negotiating for lanes on the
freeway. This AI course is a review of a selected set of these tools.
The course will cover the ideas underlying these tools, their
implementation, and how to use them or extend them in your
research.
Instructors
TAs
Administrative Assistant
| Monica Hopes | meh at cs, Wean Hall 4619 |
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Course Structure
The requirements of this course will consist of participating in
lectures, five problem sets, midterm and a project. The grading breakdown is as follows:
Problem Sets: 35%
Midterm : 30%
Final Project: 25%
Class Participation and Reading: 10%
For most of the class we will follow the book:
Artificial Intelligence: A Modern Approach 2nd Edition. by Russell and Norvig (Prentice Hall).
Some lectures will have assigned reading, and it is expected that
students read the assigned chapters before class.
Problem Sets
We will have five problem sets. Problem sets will consist of both
theoretical and programming assignments. On some assignments we will
recommend that students program in Matlab. However, you are free to do
your assignments in any programming language you like as long as you
clear it with the TAs first. We will hold a Matlab tutorial during the
first week of classes for those who are not familiar with this
language.
While you are permitted to discuss the problem sets with fellow class
members, each student must write the solutions and code on her / his
own. Problem sets are due at the beginning of class on their due date.
Late Homework Policy
In any case of personal problems preventing you from submitting your
answers on time, please contact the instructors or TAs as soon as
possible (before the problem set is due). If you do not have a good
enough reason for a late submission you will be penalized according to
the following policy:
Homework is worth full credit at the beginning of class on the due date.
It is worth 75% credit for the next 24 hours.
It is worth 50% credit if submitted more than 24 hours but less than 48 hours after due time.
It is worth zero credit after that.
You must turn in
all of the homeworks, even if for zero credit, in order to pass the course. Turn in all late homework assignments to Monica Hopes.
Midterm
The Midterm will take place during class hours (see schedule). It will cover the material in the first two parts of class.
We will be holding review sessions prior to the midterm. More information will be provided later in the class.
Projects
There are many exciting opportunities for projects in the general area of AI. Projects will either try to
extend one the methods discussed in class, apply a method to a new problem or dataset or apply a new /revised
algorithm to one of the problems discussed in class. We will provide a number of ideas for projects
but students are encouraged to propose their own ideas.
Projects will be carried out in teams of one or two (encouraged) students. You will submit a one page
proposal and a one page progress report during the semester (see class schedule for dates). The final
project presentation will be in a poster session during the final exam period. You will also be
required to submit a project report of no more than six pages during the poster session.
