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MODULE 1:INTRODUCTION
In this course you will learn to implement mathematical
ideas in machine learning. You will investigate the process of learning and
understand the application of various learning algorithms.
MODULE 2: LINEAR MODELS
Understand linear approximation and modelling of problems
and develop linear models
MODULE 3: DIMENSIONALITY REDUCTION
Use ideas from linear algebra to transform dimensions and
warp space providing additional flexibility and functionality to linear models.
MODULE 4:SVM
Develop and implement kernel based methods to develop
nonlinear models to solve few complex tasks.
MODULE 5:NEAREST NEIGHBOURS, K-MEANS, AND GAUSSIAN
MIXTURE MODELS
Review pattern recognition ideas with distance and cluster
based models to understand similarity measures and grouping criteria.
MODULE 6:NAIVE BAYES AND DECISION TREES
Dive into applications of bayes theorem and the use of
decision criteria when learning from data.
MODULE 7:SEARCH
Look at search from the perspective of graphs, trees and
heuristic based optimizations.
MODULE 8:LOGIC AND PLANNING
Discover ways to encode logic and develop agents that plan
actions in an environment.
MODULE 9:REINFORCEMENT LEARNING AND HIDDEN MARKOV MODELS
Engineering agents that learn from a sequence of actions
using rewards and penalties.
MODULE 10:Q-LEARNING AND POLICY GRADIENT
Operate in a stateful world over value and policy
approximations tasks
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