**Instructor: **B.
Ravikumar Office: 116 I, Darwin Hall

Office phone: 664 3335 Email: ravi93@gmail.com

Office hours:
T Th 9:30 to 10:30 AM, F 12 1 PM

**Class Time:
**W 6 to 8:45 PM, 2008, Salazaar
Hall

**Prerequisites for the course: **

- Data
Structures and programming ability to implement algorithms that use recursion
and recursively defined data structures (such as trees) in c++
- Mathematics
Calculus, Probability Theory
**.**

* *Chris Manning and Hinrich
Schόtze, *Foundations of Statistical Natural
Language Processing*, MIT Press.

**References**

For natural language and speech
processing

- Jurafsky & Martin,
*SPEECH and LANGUAGE PROCESSING: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition*

For general Artificial Intelligence

- Russell and Norvig,
**Artificial Intelligence: A Modern Approach**(Second Edition), Prentice-Hall.

**Course Goals**

Intelligent systems are hardware/software
systems that exhibit intelligent behavior at least in narrow contexts such as
playing a game or predicting weather. There are at least two different
approaches to designing intelligent systems. One is the symbolic approach in
which the task is modeling as a search problem and efficient algorithms to
explore the search space are devised. The other is to model the decision making
as a statistical process in which statistical models are derived from the data
that arises from the domain. This dichotomy can also be understood in terms of
whether the solution is pre-programmed or is learnt by experience. We will
primarily use the latter approach (machine-learning based approach that uses
statistical models) in the context of one problem domain namely natural
language processing. This area has become important in view of the extensive
applications of WWW and information retrieval problems in which the data is
encoded in natural language. Although our focus is in the area of natural
language processing, we will focus on techniques and models that are useful in
other domains (such as speech recognition, bio-informatics etc.)

Assigned work will include HW assignments, a programming
project, an in-class mid-term test and an in-class final exam. The HW assignments
will usually not involve programming or implementation. It may, however,
involve using software tools.

Weights
for these components are (approximately) as follows:

·
HW
assignments 20%

·
programming
project 30%

·
Mid-term
test 15%

·
Final Exam 35%