MTech Introduction To Artificial Intelligence And Machine Learning syllabus for 3 Sem 2020 scheme 20MAP334

Introduction to Data Science and AI & ML, Data Science, AI & ML, Essential Concepts in AI and ML Data Understanding, Representation and Visualisation

Machine Learning:

Linear Methods, Linear Regression, Multiple Linear Regression, Non-Linear Regression, Clustering, Forecasting models, Perceptron and Neural Network, Decision Trees, Support Vector Machines.

Probabilistic Models, Dynamic programming and Reinforcement Programming, Evolutionary Algorithms, Time Series Models, Deep Learning, Emerging Trends in ML, Unsupervised Learning

Foundations for AI, AI Basics , AI Classification, Supervised Learning, Feature Engineering Regression, Model Selection, Model Performance , Ranking

Introduction to ML with R and using Python, Python and R for Artificial Intelligence, Machine Learning, and Data Science, AI/ML in aerospace industry

Course outcomes:

At the end of the course the student will be able to:

Question paper pattern:

The SEE question paper will be set for 100 marks and the marks scored will be proportionately reduced to 60.

• The question paper will have ten full questions carrying equal marks.
• Each full question is for 20 marks.
• There will be two full questions (with a maximum of four sub questions) from each module.
• Each full question will have sub question covering all the topics under a module.
• The students will have to answer five full questions, selecting one full question from each module.

Textbook/ Textbooks

1 Machine Learning and Artificial Intelligence Ameet V Joshi Springer 2019

2 Artificial Intelligence and Machine Learning fundamentals Zsolt Nagy Packt Publishing 2018

Reference Books

1 Artificial Intelligence and Machine Learning Vinod Chandra SS PHI Learning 2014

2 Basics of Artificial Intelligence and Machine Learning DheerajMehrotra Notion Press 2019