• Understand linear approximation and modelling of problems and develop linear models
  • Use ideas from linear algebra to transform dimensions and warp space providing additional flexibility and functionality to linear models.
  • Develop and implement kernel based methods to develop nonlinear models to solve few complex tasks.
  • Review pattern recognition ideas with distance and cluster based models to understand similarity measures and grouping criteria.
  • Dive into applications of bayes theorem and the use of decision criteria when learning from data.
  • Look at search from the perspective of graphs, trees and heuristic based optimizations.
  • Discover ways to encode logic and develop agents that plan actions in an environment.
  • Engineering agents that learn from a sequence of actions using rewards and penalties.
  • Operate in a stateful world over value and policy approximations tasks

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