Artificial Intelligence CONS ZG 551
Second Semester, 2004-2005
General Information
Course Description
The field of Artificial Intelligence (AI) has a natural connection with consciousness. The field aims to develop computational methods of a variety of cognitive tasks such as machine learning, problem solving, robotics and control. After introducing symbolic and connectionist computational models, several approaches to machine learning and data mining will be introduced. The topics of the discovery of patterns from data and pattern classification will be introduced and their application will be discussed. The course consists of a laboratory module that demonstrates simple applications of AI techniques. In another part of the course, students will be asked to critique/discuss research papers in AI.
Evaluation Components:
|
Component |
Marks |
|
Home assignment 1 |
10 |
|
Oral Presentation 1 |
10 |
|
Quiz 1 |
5 |
|
Midterm |
20 |
|
Home assignment 2 |
10 |
|
Oral Presentation 2 |
10 |
|
Quiz 2 |
5 |
|
Comprehensive Exam |
30 |
|
Total |
100 |
Number of hours: 43 hours
|
No. of Lectures |
Lecture Modules |
|
6 |
Computational models Computer organization: hardware, software. Programming languages: C, C++, perl. |
|
3 |
Computation, Universal Turing machine and Halting Problem, Church-Turing hypothesis. |
|
3 |
Quantum computing: logic gates for classical and quantum bits; factorization.
|
|
6 |
Connectionist computational model: Hebbian rule, Kohonen's SOM. Perceptron, MLP, Back Propagation algorithm. Machine learning and Data mining |
|
4 |
Supervised, Unsupervised and Reinforcement learning. Clustering: hierarchical, k-means. Decision trees, Search algorithms. |
|
3 |
Pattern Classification: Distance, Gaussian Mixture Models. Matching sequences; Dynamic Programming, hidden Markov models. |
|
3 |
Applications: Speech/Handwriting recognition, Bioinformatics
|
|
3 |
Natural Language Processing: Automatic Text Classification, FST for text extraction. NLP parsing. Machine Translation. |
|
3 |
Laboratory sessions: Programming (C), Pattern matching (perl), Decision tree (C4.5) |
The following papers were read and analyzed by the students, and then an oral presentation was made to the class:
Supplemental Books:
Duda, R. O.; Hart, P.E.; Stork, D.G., 2000, Pattern
Classification 2nd Edition with Computer Manual.
Jurafsky, D. and Martin, J.H., 2000, An introduction to
NLP, Computational Linguistics, and Speech Recognition, Pearson
Education Asia.
Nielsen, M.A. and Chuang, I.L. 2000, Quantum Computation
and Quantum Information, Cambridge University Press.
O'Shaughnessy, D.,
2001, Speech Communication Human and Machine, 2nd edition, University
press, Hyderabad.
Tveter, D., 1998, The Pattern Recognition Basis of
Artificial Intelligence, Wiley-IEEE Computer Society Press.
Supplemental Papers: