Courses Detail Information
ECE6702J – Problem Solving with AI Techniques
Instructors:
Credits:
Pre-requisites: Necessary computer science and mathematical background covered in VE281, VE203, VV216/256/286
Description:
This course presents a selection of artificial intelligence techniques that have proved to be efficient to solve
general problems (notably reasoning/planning, learning or decision-making). Although the emphasis is on
how to use those tools, relevant theoretical aspects are also overviewed to provide a solid foundation for their
effective applications.
Course Topics:
Introduction to Al;
presentation of course contentIntroduction to reasoning under certaintyDeterministic Search algorithmsStochastic search(notably, MCTS)Application: SearchRefresher on Probability
TheoryIntroduction to reasoning under uncertainty
Bayesian networks
Markov models
Hidden Markov models
Application: Reasoning under uncertainty
Introduction to machine learning
Linear and logistic regressions
Neural networks
Convolutional and recurrent neural networksEnsemble methods:bagging and boostingApplication: Machine learning
Introduction to Sequential decision-making
Markov decision process
Reinforcement learning
Deep reinforcement learning
Application:Sequential decision-making