Machine Learning (AL-405) - Official Syllabus

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Unit 1

Introduction to machine learning

Introduction to machine learning
scope and limitations
machine learning models
Supervised Learning
Unsupervised Learning
hypothesis space and inductive bias
evaluation
cross-validation
Dimensionality Reduction: Subset Selection
Shrinkage Methods
Principle Components Analysis
Partial Least Squares

Unit 2

Neural Networks

Neural Networks: From Biology to Simulation
Neural network representation
Neural Networks as a paradigm for parallel processing
Perceptron Learning
Training a perceptron
Multilayer perceptron
back propagation Algorithm
Training & Validation
Activation functions
Vanishing and Exploding Gradients

Unit 3

Supervised Learning Techniques

Decision Trees
Naive Bayes
Classification
Support vector machines for classification problems
Random forest for classification and regression problems
Linear regression for regression problems
Ordinary Least Squares Regression
Logistic Regression

Unit 4

Unsupervised Learning, Clustering

Unsupervised Learning
Clustering: k-means
adaptive hierarchical clustering
Gaussian mixture model
Optimization Using Evolutionary Techniques
Number of Clusters
Advanced discussion on clustering
Expectation Maximization

Unit 5

Design and Analysis of Machine Learning Experiments

Factors, response and strategy of experimentation
Guidelines for machine learning experiments
cross-validation and resampling methods
Measuring classifier performance
Hypothesis testing
comparing multiple algorithms
comparison over multiple datasets

Laboratory Experiments

How to calculate important numbers based on data sets, how to use various Python modules and how to make functions that are able to predict the outcome based on what we have learned (Small Dataset)
How can we get Big Data Sets, Learn: Data Distribution, Normal data distribution, Random Data Distribution, Scatter Plot.
Build an Artificial Neural Network by implementing the Backpropagation algorithm and test the same using appropriate data sets.
The probability that it is Friday and that a student is absentis 3%. Since there are 5 school days in a week,the probability that it is Friday is 20%. What is the probability that a student is absent given that today is Friday? Apply Baye’s rule in python to get the result.
Write a program to implement k-Nearest Neighbour algorithm to classify the iris data set.
Implement and demonstrate simple Linear Regression Algorithm based on a given set of training data samples. Read the training data from a .CSV file.
Implement and demonstrate Logistic Regression (Binomial) based on a given set of training data samples. Read the training data from a .CSV file.
Implement and demonstrate naïve Bayesian classifier for a sample training data set stored as a .CSV file. Compute the accuracy of the classifier, considering few test data sets.
Write a program to demonstrate the working of the decision tree based ID3 algorithm. Use an appropriate data set for building the decision tree and apply this knowledge to classify a new sample.
Given the following data, which specify classifications for nine combinations of VAR1 and VAR2 predict a classification for a case where VAR1=0.906 and VAR2=0.606, using the result of k-means clustering with 3 means (i.e., 3 centroids)

Reference Books

Machine Learning — Tom Mitchell — McGraw- Hill — First Edition, 1997
Introduction to Machine Learning — Ethem Alpaydin — Edition 2
Introduction to Machine learning — Nils J. Nilsson
Machine learning for dummies — Judith Hurwitz, Daniel Kirsch — IBM Limited ed
Introduction to Machine Learning with Python A guide for data scientists — Andreas, C. Muller, Sarah Guido — O'Reilly

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