Course Contents
The course is designed to cover advanced machine learning algorithms for supervised and un-supervised learning
Course Synopsis
• The course aims to provide a deep understanding of machine learning techniques and its practical application.
Course Learning Outcomes
Upon completion of this course, students will:
• Know the difference between supervised and unsupervised learning
• Be able to apply machine learning algorithms to any data
• Be able to analyze the performance of algorithms (image processing, text processing, data mining etc.)
Basic Introduction to Machine Learning
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A comprehensive introduction to Machine Learning
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Supervised Learning
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Unsupervised Learning
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Taxonomy of learning tasks: Supervised /Classification, Unsupervised/Clustering and Reinforcement Learning
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Decision Trees Introduction
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Decision Tree Example
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Quinlan's ID3 Algorithm
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Decision Tree Split Purity
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Decision Tree Entropy
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Information Gain
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Overfitting in Decision Trees
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Decision Tree Pruning
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Rule induction
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Overview of Bayesian learning
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Bayesian classification
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Class model and the prior
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Role of denominator in Naive Bayes
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Probabilistic classifiers: generative vs discriminative
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Independence assumption in Naive Bayes
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Mutual independence vs conditional independence
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Naive Bayes for spam detection
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Instance based learning
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K nearest neighbour
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Linear regression vs Multiple regression
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Performance Evaluation
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Model Evaluation & Selection
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Confusion Matrices & Basic Evaluation Metrics
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Model Selection: Optimizing Classifiers for Different Evaluation Metrics
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Normal data distribution
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Binomial distribution
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Chi Square test
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Phi coefficient
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Support vector machine 1
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Support vector machine 2
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Unsupervised learning
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Clustering
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K-means clustering
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Agglomerative hierarchical clustering
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Neural Networks
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kohenen nets
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Back propagation networks
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Association rules ( Apriori algorithm)
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Reinforcement learning
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Q-learning
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Hidden Markov Models 1
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Hidden Markov Models 2
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Ensemble methods
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Bagging
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Boosting 1
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Boosting 2
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Stacking
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Book Title : Machine Learning
Author : Tom Mitchell
Edition :
Publisher : McGraw-Hill
Book Title : Pattern Recognition and Machine Learning
Author : Christopher Bishop
Edition :
Publisher : Springer
Book Title : Pattern Classification
Author : Richard Duda, Peter Hart and David Stork
Edition : 2nd ed
Publisher : John Wiley & Sons
Book Title : Reinforcement Learning: An introduction
Author : Richard Sutton and Andrew Barto
Edition :
Publisher : MIT Press
Title : Lecture Details Object oriented Programming
Type : Scheme of Study
View Lecture Details Object oriented Programming