RAJIV GANDHI PROUDYOGIKI VISHWAVIDYALAYA, BHOPAL
New Scheme Based On AICTE Flexible Curricula
CSE-Artificial Intelligence and Machine Learning/ Artificial Intelligence and Machine Learning
IV-Semester
AL405 Machine Learning
Course Objectives: This course provides a broad introduction to machine learning. It offers some of the
most cost-effective approaches to automated knowledge acquisition in emerging data-rich disciplines and
focuses on the theoretical understanding of these methods, as well as their computational implications.
To provide an understanding of the theoretical concepts of machine learning and prepare students for
research or industry application of machine learning techniques
Unit I :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 II :Neural Networks: From Biology to Simulation, Neural network representation, Neural Networks
as a paradigm for parallel processingPerceptron Learning, Training a perceptron, Multilayer perceptron,
back propagation Algorithm, Training & Validation,Activation functions, Vanishing and Exploding
Gradients.
Unit III :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 IV :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 V :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
Books and references :
1. Machine Learning. Tom Mitchell. First Edition, McGraw- Hill, 1997.
2. Introduction to Machine Learning Edition 2, by EthemAlpaydin
3. Introduction to Machine learning, Nils J.Nilsson
4. Machine learning for dummies, IBM Limited ed, by Judith Hurwitz and Daniel Kirsch
5. Introduction to Machine Learning with Python A guide for data scientists, Andreas, C. Muller &
Sarah Guido, O'Reilly
List of Experiments:
Different problems to be framed to enable students to understand the concept learnt and get hands-on on
various tools and software related to the subject
New Scheme Based On AICTE Flexible Curricula
CSE-Artificial Intelligence and Machine Learning/ Artificial Intelligence and Machine Learning
IV-Semester
AL405 Machine Learning
Course Objectives: This course provides a broad introduction to machine learning. It offers some of the
most cost-effective approaches to automated knowledge acquisition in emerging data-rich disciplines and
focuses on the theoretical understanding of these methods, as well as their computational implications.
To provide an understanding of the theoretical concepts of machine learning and prepare students for
research or industry application of machine learning techniques
Unit I :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 II :Neural Networks: From Biology to Simulation, Neural network representation, Neural Networks
as a paradigm for parallel processingPerceptron Learning, Training a perceptron, Multilayer perceptron,
back propagation Algorithm, Training & Validation,Activation functions, Vanishing and Exploding
Gradients.
Unit III :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 IV :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 V :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
Books and references :
1. Machine Learning. Tom Mitchell. First Edition, McGraw- Hill, 1997.
2. Introduction to Machine Learning Edition 2, by EthemAlpaydin
3. Introduction to Machine learning, Nils J.Nilsson
4. Machine learning for dummies, IBM Limited ed, by Judith Hurwitz and Daniel Kirsch
5. Introduction to Machine Learning with Python A guide for data scientists, Andreas, C. Muller &
Sarah Guido, O'Reilly
List of Experiments:
Different problems to be framed to enable students to understand the concept learnt and get hands-on on
various tools and software related to the subject
