Semester – VII:       Departmental Elective – V:        Specialization – Automation and Industry 4.0 Subject Code: KME 074             Machine Learning  

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  • Complete coverage to the syllabus 
  • Best Explanation in Easiest Language
  • No extra words and easy to learn, Prepare your subject

Note: in the syllabus given below click on the topic you want to study or to make notes of for your exams.

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 Unit 1: Introduction to Machine Learning (6Hours)

 An Introduction toMachine Learning, Types of Machine Learning, and Applications of ML inMechanical Engineering, Designing a Learning System, Performance Measures for ML Model, Issues in Machine Learning, AI vs. ML, and Essential Math for ML and AI, Data Science Vs Machine Learning

Unit 2: Supervised Learning (9Hours)

 Supervised Learning:  Introduction to Supervised Learning, Classification, Regression Analysis and its Types, Model Selection Procedures, Bayesian Decision Theory, Naïve Bayes Classifier, Bayes Optimal Classifier,

Evaluating an Estimator: Bias and Variance, Support Vector Machines, Types of Support Vector Kernel (Linear Kernel, Polynomial Kernel, Gaussian Kernel, Issues in SVM, Case Study on House Price Prediction using Machine Learning.

Unit 3: Unsupervised Learning (9Hours)

 Unsupervised Learning: Introduction to Unsupervised Learning, Cluster Analysis, K-Means Clustering, Expectation-Maximization Algorithm,

Dimensionality Reduction: Principal Components Analysis, Independent Component Analysis, Multidimensional Scaling, Linear Discriminant Analysis.

Unit 4: Decision Tree & Neural Networks (9Hours)

Decision Trees: Basics of Decision Tree, Issues in Decision tree learning, ID3 Algorithm, Information gain and Entropy.

Introduction to Neural Networks: Perceptron, The Back propagation Algorithm, The Convergence analysis and universal approximation theorem for back propagation algorithm, Concept of Convolution Neural Networks, Types of Layers of CNN, Case Study of CNN (either on Self driving car, Building a smart speaker, etc.)

Unit 5: Genetic Algorithms & Reinforcement Learning (7Hours)

Genetic Algorithm: Introduction, Components of Genetic Algorithm, CrossOver, Mutation, Model of Evolution and Learning, Applications of Genetic Algorithm.

Reinforcement Learning: Introduction to Reinforcement Learning, Learning task, Model-Based Learning Q- Learning, Markov Decision Process, Q Learning Function, Temporal Difference Learning, Generalization.

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