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Machine Learning and Artificial Intelligence (AI)
In this course you will learn to implement mathematical ideas in Artificial intelligence. You will investigate the process of learning and understand the application of various learning algorithms.
Prerequisites:
The courses assignments and notes will use python programming language and expects a basic knowledge of python. We assume the student has completed the Machine Learning Foundations or has an equivalent fluency in mathematics and fundamentals.
course details :
Linear Models
Understand linear approximation and modelling of problems and develop linear models
Dimensionality Reduction
Use ideas from linear algebra to transform dimensions and warp space providing additional flexibility and functionality to linear models.
SVM
Develop and implement kernel based methods to develop nonlinear models to solve few complex tasks.
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.
Naive Bayes and Decision Trees
Dive into applications of bayes theorem and the use of decision criteria when learning from data.
Search
Look at search from the perspective of graphs, trees and heuristic based optimizations.
Logic and Planning
Discover ways to encode logic and develop agents that plan actions in an environment.
Reinforcement Learning and Hidden Markov Models
Engineering agents that learn from a sequence of actions using rewards and penalties.
Q-Learning and Policy gradient
Operate in a stateful world over value and policy approximations tasks
The courses assignments and notes will use python programming language and expects a basic knowledge of python. We assume the student has completed the Machine Learning Foundations or has an equivalent fluency in mathematics and fundamentals.
course details :
Linear Models
Understand linear approximation and modelling of problems and develop linear models
Dimensionality Reduction
Use ideas from linear algebra to transform dimensions and warp space providing additional flexibility and functionality to linear models.
SVM
Develop and implement kernel based methods to develop nonlinear models to solve few complex tasks.
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.
Naive Bayes and Decision Trees
Dive into applications of bayes theorem and the use of decision criteria when learning from data.
Search
Look at search from the perspective of graphs, trees and heuristic based optimizations.
Logic and Planning
Discover ways to encode logic and develop agents that plan actions in an environment.
Reinforcement Learning and Hidden Markov Models
Engineering agents that learn from a sequence of actions using rewards and penalties.
Q-Learning and Policy gradient
Operate in a stateful world over value and policy approximations tasks
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