CS 365: Artificial Intelligence
Pre-requisites: ESC101, CS210, CS202
- AI: Introduction
- Brief history.
- Agents and rationality, task environments, agent architecture types.
- Search and Knowledge representation.
- Search spaces.
- Uninformed and informed search.
- Hill climbing, simulated annealing, genetic algorithms.
- Logic based representations (PL, FoL) and inference, Prolog.
- Rule based representations, forward and backward chaining, matching algorithms.
- Probabilistic reasoning and uncertainty.
- Bayes nets and reasoning with them.
- Uncertainty and methods to handle it.
- Forms of learning.
- Statistical methods: naive-Bayes, nearest neighbour, kernel, neural network models, noise and overfitting.
- Decision trees, inductive learning.
- Clustering - basic agglomerative, divisive algorithms based on similarity/dissimilarity measures.
- Applications to NLP, vision, robotics, etc.
Books and References:
- Stuart Russell, Peter Norvig, Artificial Intelligence: A Modern Approach, 3rd Ed., Prentice Hall, 2009. Can also use 2nd Ed., Pearson Education International, 2003.
- Nils Nilsson, Artificial Intelligence: A New Synthesis, Morgan Kaufmann, 1998.
- David Poole, Alan Mackworth, Artificial Intelligence: Foundations for Computational Agents, Cambridge Univ. Press, 2010.
Other References: 1. Ronald Brachman, Knowledge Representation and Reasoning, Morgan Kaufmann, 2004. 2. Frank van Harmelen, Vladimir Lifschitz, Bruce Porter (Eds), Handbook of Knowledge Representation, Elsevier, 2008. 3. Ivan Bratko, Prolog Programming for Artificial Intelligence, 4th Ed., Addison-Wesley, 2011. 4. Stephen Marsland, Machine Learning: An Algorithmic Perspective, Chapman and Hall, 2009. 5. Christopher Bishop, Pattern Recognition and Machine Learning, Springer, 2007.