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# Introduction to Machine Learning

With the increased availability of data from varied sources there has been increasing attention paid to the various data driven disciplines such as analytics and machine learning. In this course we intend to introduce some of the basic concepts of machine learning from a mathematically well motivated perspective. We will cover the different learning paradigms and some of the more popular algorithms and architectures used in each of these paradigms.

The tables below enlists the courses materials for Week 1 to Week 12. Each topic has both YouTube link and VideoKen link.

## Week 1 - Introduction to Machine Learning & Probability Theory

 A brief introduction to machine learning Supervised learning Unsupervised learning Reinforcement learning Probability tutorial - 1 Probability tutorial - 2 Assignment 1 Solution 1

## Week 2 - Linear Algebra, Statistical Decision Theory, Linear Regression & Dimensionality Reduction

 Linear algebra tutorial - 1 Linear algebra tutorial - 2 Statistical decision theory - regression Statistical decision theory - classification Bias-variance Linear regression Multivariate regression Subset selection - 1 Subset selection - 2 Shrinkage methods Principal components regression Partial least squares Assignment 2 Solution 2

## Week 3 - Classification: Linear Models

 Linear classification Logistic regression Linear discriminant analysis - 1 Linear discriminant analysis - 2 Linear discriminant analysis - 3 Weka tutorial Assignment 3 Assignment 3 data Solution 3

## Week 4 - Optimisation & Classification: Separating Hyperplane Approaches

 Optimisation tutorial Perceptron learning SVM - formulation SVM - interpretation & analysis SVMs for linearly non-separable data SVM kernels SVM hinge loss formulation Assignment 4 Assignment 4 data Solution 4

## Week 5 - Artificial Neural Networks & Parameter Estimation

 Early artificial neural network models Backpropogation - 1 Backpropogation - 2 Initialisation, training & validation Maximum likelihood estimate Priors & MAP estimate Bayesian parameter estimation Assignment 5 Solution 5

## Week 6 - Decision Trees

 Introduction to decision trees Regression trees Stopping criteria & pruning Loss functions for classification Categorical attributes Multiway splits Missing values, imputation & surrogate splits Instability, smoothness & repeated subtrees Decision trees tutorial Assignment 6 Assignment 6 data Solution 6

## Week 7 - Evaluation Measures & Hypothesis Testing

 Evaluation measures Bootstrapping & cross validation 2 class evaluation measures The ROC curve Minimum description length & exploratory analysis Introduction to hypothesis testing Hypothesis testing: basic concepts Sampling distribution & the Z test Student's T test The two sample & paired sample T tests Confidence intervals Assignment 7 Solution 7

## Week 8 - Ensemble Methods

 Bagging, committee machines & stacking Boosting Gradient boosting Random forest Assignment 8 Solution 8

## Week 9 - Graphical Models

 Naive Bayes Bayesian networks Undirected graphical models - introduction Undirected graphical models - potential functions Hidden Markov models Variable elimination Belief propogation Assignment 9 Solution 9

## Week 10 - Clustering

 Partitional clustering Hierarchical clustering Threshold graphs The BIRCH algorithm The CURE algorithm Density based clustering Assignment 10 Solution 10

## Week 11 - Gaussian Mixture Models, Spectral Clustering & Learning Theory

 Gaussian mixture models Expectation maximisation - 1 Expectation maximisation - 2 Spectral clustering Learning theory Assignment 11 Solution 11

## Week 12 - Frequent Itemset Mining & Reinforcement Learning

 Frequent itemset mining The apriori property Introduction to reinforcement learning RL framework & TD learning Solution methods & applications Miscellaneous: Multi-class classification Assignment 12 Solution 12