CS
480 Artificial Intelligence
Fall
2009
Instructor
B.
Ravikumar
Office:
116 I, Darwin Hall
Office
phone: 664-3335
Email:
ravi93@gmail.com
Office
hours: M 1 – 2, T 11 – 12 and by
appointment
Class
Time and place:
Friday 9 to 12 noon,
Catalog Description:
A survey of techniques that
simulate human intelligence.
Topics may include: pattern recognition, general problem solving, adversarial
game-tree search, decision-making, expert systems, neural networks, fuzzy
logic, and genetic algorithms. Prerequisite: CS 315 or consent of instructor.
Background Expected:
- Discrete mathematics (CS 242)
- Linear algebra
(basics)
- CS 315
- Some background in logic and probability will
be helpful, but not necessary.
Course Goals:
Artificial Intelligence
(AI) is defined as the science of programming computers to perform tasks that
would seem to require (human) intelligence. The range of such tasks is very wide
– understanding language, vision and speech processing, problem solving,
planning and the most difficult of all – common sense reasoning. AI techniques
are wide ranging as well – from heuristic programming to simulation of (human)
intelligence through various machine learning techniques. We will learn to
model state space of discrete systems (such as in board games) and various
search techniques. We will also discuss symbolic processing techniques (based
on logical deduction) and show how they can be used for planning and reasoning.
We will also consider statistical alternatives that have become very successful
in areas like natural language processing and speech recognition. Finally,
machine learning techniques such as neural networks will be discussed in
various applications such as hand-written character recognition. Some new
programming techniques and models (functional and logic programming) will be
introduced as appropriate.
Text:
Artificial
Intelligence, A Modern Approach, Russell and Norvig,
Prentice
Hall, Second Edition.
Course outline:
· Course overview (Chapters 1 and 2)
·
State-space
representation and searching
o
Solving
Problems by Searching (Ch 3)
o
Informed Search and Exploration (Ch 4)
o
Adversarial
Search (Ch 6)
·
Symbolic
approach to reasoning
o
First-Order
Logic
(Ch 8)
o Inference in First-Order Logic (Ch 9)
o
Knowledge
Representation (Ch 10)
·
Uncertain
Knowledge and Reasoning
o
Probabilistic
Reasoning (Ch 14)
o
Probabilistic
decision making (Ch 16 and Ch 17)
·
Machine
Learning
o
Neural
networks, statistical learning techniques (Ch 20)
·
Communicating,
Perceiving, and Acting
o
Computer
vision (Ch 24)
Course Work and Evaluation:
Course work will include:
·
Two
Mid-Term tests (20%) – Both tests
will be in class and will be about 75 minutes long. The tests will be open
book/open notes.
·
Projects (50%) – There will be some common programming
projects and a final project. Some possible common projects:
Game-tree search
Solitaire (e.g. sliding piece puzzle,
peg-solitaire)
Adversarial search (e.g. backgammon)
Neural network application (e.g. medical
diagnosis)
Image analysis (e.g. hand written character
recognition)
Decision tree/Bayesian net
Hidden Markov model
Formal logic and deduction
The
final project will be done individually. You can choose a problem from a list
that will be provided early in the semester. The project is due the last week
of the semester. You are to write a report summarizing your contributions to
the chosen problem and present it. Some selected project work will be presented
in the department colloquium.
·
Final
Examination (30%) – The final
examination will be comprehensive and will take place at the scheduled time posted
in the web page http://www.sonoma.edu/university
/classsched/ finals_sched.pdf
