Course

Unit 1

Introduction: Machine learning, Terminologies in machine learning, Types of machine learning: supervised, unsupervised, semi-supervised learning.

Unit 2

Basic parametric models for regression and classification: Linear regression, Classification and logistic regression, Polynomial regression, and regularization, and generalized linear models. Understanding, evaluating, and improving the performance

Unit 3

Neural networks and deep learning: The neural network model, Training a neural network, Convolutional neural networks, and Dropout. Ethics in Machine learning. Application: Airfoil design through neural network

Course Objectives

This course provides the foundations of Machine Learning.

• Gain an overview of cluster analysis process and cluster quality evaluation
• Design and performance evaluation of classifiers for typical classification
• Apply the concepts of machine learning to aerospace

Course Outcomes

CO1: Generate, analyse and interpret data summaries,

CO2: Carry out analysis using machine learning algorithms,

CO3: Design and implement classifiers for machine learning applications,

CO-PO Mapping

Evaluation Pattern

• CA – Can be Quizzes, Assignment, Projects, and Reports

Text Book(s)

Lindholm, Andreas, Niklas Wahlström, Fredrik Lindsten, and Thomas B. Schön. Machine learning: a first course for engineers and scientists. Cambridge University Press, 2022.

Reference(s)

Kevin P. Murphy, “Machine Learning, a probabilistic perspective”, The MIT Press Cambridge, Massachusetts, 2012. Alex Smola and SVN. Viswanathan, “Introduction to Machine Learning”, Cambridge University Press, 2008.

Introduction to Machine Learning | Nils J. Nilsson, Stanford University.