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IE2xx : Introduction to Artificial Intelligence and Machine Learning

Prerequisites topics

Probability, statistics, linear algebra

Contents:

  • Artificial agents - nature and structure
  • Search based problem-solving
  • Uninformed search and heuristic search
  • Local search
  • Adversarial search and constraint satisfaction
  • Propositional logic, first order logic and knowledge representation using semantic nets, taxonomy trees
  • Uncertain knowledge representation: Bayesian nets, causal nets, inference, temporal models like HMM, dynamic Bayesian nets
  • Planning: Heuristics, Hierarchical planning, planning in uncertain domains, scheduling
  • Decision networks, sequential decision making, multi-agent decision making¬†
  • Learning from examples: regression and classification
  • Deep learning models for computer vision and NLP
  • Basics of Reinforcement learning¬†
  • Robotics - perception, planning and control, uncertain movements¬†
  • Humans and AI - impact, ethics, limits, laws

References

  • Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach. Pearson, Fourth Edition, 2020.
  • Richard E. Neapolitan and Xia Jiang. Artificial Intelligence with an Introduction to Machine Learning, CRC Press, Second Edition, 2018.
  • Tom Mitchell. Machine Learning, McGraw Hill Education, First edition, 2017.
  • Richard Duda, Peter Hart and David Stork. Pattern Classification, Wiley Interscience, Second Edition, 2007.