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CONS ZG551: Artificial Intelligence

General Information

Instructor: Prof. Samudravijaya K.
Time: Thursdays 2 p.m.-6 p.m.
Venue: #207, Bhaktivedanta Institute

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:

  • Barker-Plummer, D. 2005, "Turing Machines", The Stanford Encyclopedia of Philosophy (Spring 2005 Edition), Edward N. Zalta (ed.), URL = <http://plato.stanford.edu/archives/spr2005/entries/turing-machine/>.
  • Chomsky, N., 1993,  "On the nature, use and acquisition of language" in Readings in philosophy and cognitive sciences edited by A. Goldman, Chapter 23.
  • Kearn, J.T., 1997, "Thinking machines: Some fundamental confusions" Mind and Machines Vol 7, No.2, May
  • Harnad, S., 1992, "Connecting object to symbol in modeling cognition", In Connectionism in Context, edited by Andy Clark and Rudi Lutz.
  • Hardcastle, V., 1995, "A critique of information processing theories of consciousness" Mind and Machines Vol. 5 Feb
  • Fetzer, J., 1997, "Thinking and computer: computers as a special kind of sign" Mind and Machines Vol 7 no.3
  • Kaplan, S.;  Weaver, M.; French, R. M., 1992, “Active Symbols and Internal Models: Towards a Cognitive Connectionism” in Connectionism in Context, edited by Andy Clark and Rudi Lutz.
  • T. Winograd; F. Flores, 1987, "Computation, Thought and Language" in Understanding Computers and Cognition. Addison-Wesley.
  • T. Winograd, 1991, "Thinking Machines: Can there be? Are We?" in The Boundaries of Humanity: Humans, Animals, Machines, edited by James Sheehan and Morton Sosna,.Berkeley: University of California Press, pp. 198-223. http://hci.stanford.edu/winograd/papers/thinking-machines.html

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:

  • Allen, J.B., 1994, "How do humans process and recognize speech?" IEEE Transactions on Speech and Audio Processing, vol 2 (4), pp. 567-577.
  • Boden, M. A., 1990, “Escaping From the Chinese Room” In The Philosophy of Artificial Intelligence, M. A. Boden (Ed.).
  • Brill, E.; Mooney, R.J. 1997, “An overview of empirical natural language processing”, AI Magazine Vol. 18(4), pp. 13-24.
  • Cardie, C., 1997, “Empirical Methods in Information Extraction”, AI Magazine, Vol.18(4), pp. 65-79.
  • Charniak, E., 1997, “Statistical Techniques for Natural Language Parsing”, AI Magazine, Vol.18(4), pp. 33-43.
  • Hayes-Roth, F. 1997, “Artificial Intelligence, What Works & What Doesn’t” ,  AI Magazine, Vol. 18(2),  pp 99-114.
  • Joshi, A. K., 1998, “Relationship between Natural Language Processing and AI” AI Magazine, vol. 19(3), pp. 95-107.
  • Knight, K., 1997, “Automating Knowledge acquisition for Machine translation”  AI magazine Vol. 18(4), pp. 81-96.
  • Macredie, R.D. and Coughlan, J., 2004, “Human-computer interfaces; a principled approach to design", Eds: C. Sandom and R. S. Harvey in Human Factors for Engineers, IEE London pp. 235-236.
  • Minsky, M. “Steps Towards Artificial Intelligence”, Ch3, Computation and Intelligence, George F. Luger (Ed.).
  • McClelland, J.L., Rumelhart, D.E. and Hinton, G.E., 1986, “The appeal of parallel Distributed Processing” –From chapter 1 of “ Parallel Distributed Procesing” Vol. 1, MIT press.
  • Newell, A. and Simon, H.A. 1995, “Computer Science as Empirical Inquiry: Symbols and Search” In Computation and Intelligence, edited by George F. Luger Chapter 4.
  • Ng, H.T.; Zelle, J., 1997, “Corpus-based Approaches to Semantic Interpretation in NLP”, AI Magazine, Vol. 18(4), pp. 45-64.
  • Noyes, J.; Garland K. and Bruneau, D., 2004, "Humans: skills, capabilities and limitation', Eds: C. Sandom and R.S. Harvey, IEE London, pp. 35-56.
  • Oviatt, S., 2003, "User-Centered Modeling and Evaluation of Multimodal Interfaces" Proc. IEEE, pages 1457-1468.
  • Pribram, K. H. (ed), 1993, Rethinking Neural Networks: Quantum Fields and Biological Data,), Erlbaum, NY.
  • Searle, J. R. “Minds, Brains and Programs” In The Philosophy of Artificial Intelligence, edited by M. A. Boden.
  • Selfridge, O. and Niesser, U., 1995, “Pattern recognition by machines”, In Computers and Thought, edited by Edward Feigenbaum and Julian Feldman, Section 6.
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