Department of Mathematicscoretheory
Artificial Intelligence
MAT 3132
Syllabus
- 01Introduction to Artificial Intelligence: Brief history of AI. Agents and rationality, task environments, agent architecture types
- 02Search and Knowledge representation. Search spaces Uninformed and informed search
- 03Learning – knowledge in learning, logical formulation; statistical learning, complete data, hidden variables; reinforcement learning, passive and active
- 04Unsupervised Learning: Clustering, Dimension reduction, Expectation Maximization, Mixture of Gaussians, Hidden Markov Models, Anomaly detection
- 05Techniques of Artificial Intelligence: Hill climbing, simulated annealing, genetic algorithms
- 06Logic based representations (PL,FoL) and inference, Prolog
- 07Rule based representations, forward and backward chaining, matching algorithms
- 08Probabilistic reasoning and uncertainty. Bayes nets and reasoning with them
- 09Learning of Artificial Intelligence: Uncertainty and methods to handle it. Forms of learning. Statistical methods: Naive-Bayes, nearest neighbour, kernel, neural network models, noise and over fitting
- 10Decision trees, inductive learning
- 11Clustering - basic agglomerative, divisive algorithms based on similarity/dissimilarity measures
- 12Applications to Natural Language Processing, vision, robotics, etc.
References
- Tom Mitchell. Machine Learning. McGraw Hill, 1997.
- Machine Learning: A Probabilistic Perspective, Kevin P Murphy, MIT Press, 2012.
- Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer 2006.
- Richard O. Duda, Peter E. Hart, David G. Stork. Pattern Classification. John Wiley & Sons, 2006.
- Trevor Hastie, Robert Tibshirani, Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer 2009.
- MacKay, David. Information Theory, Inference, and Learning Algorithms. Cambridge, UK: Cambridge University Press, 2003.
- Russel, S., and Norvig, P., (2015), Artificial Intelligence: A Modern Approach, 3rd Edition, Prentice Hall
- Lang, Q. (1997), Intelligent Planning: A decomposition and abstraction-based approach, Springer Verlag, Berlin Heidelberg.
Credits Structure
3Lecture
0Tutorial
0Practical
3Total