EI5014: Machine Learning Syllabus for EIE 8th Sem 2019 Regulation Anna University (Professional Elective-VII)

Machine Learning detailed syllabus for Electronics & Instrumentation Engineering (EIE) for 2019 regulation curriculum has been taken from the Anna Universities official website and presented for the EIE students. For course code, course name, number of credits for a course and other scheme related information, do visit full semester subjects post given below.

For Electronics & Instrumentation Engineering 8th Sem scheme and its subjects, do visit EIE 8th Sem 2019 regulation scheme. For Professional Elective-VII scheme and its subjects refer to EIE Professional Elective-VII syllabus scheme. The detailed syllabus of machine learning is as follows.

Course Objective:

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Unit I

Introduction To Machine Learning
Objectives of machine learning – Human learning/ Machine learning – Types of Machine learning:-Supervised Learning – Unsupervised learning – Regression – Classification – The Machine Learning Process:- Data Collection and Preparation – Feature Selection – Algorithm Choice -Parameter and Model Selection – Training – Evaluation – Bias-Variance Tradeoff – Underfitting and Over fitting Problems.

Unit II

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Unit III

Supervised Learning
Linearly separable and nonlinearly separable populations – K-Nearest Neighbor – Logistic Regression – Radial Basis Function Network – Support Vector Machines: – Kernels – Risk and Loss Functions – Support Vector Machine Algorithm – Multi Class Classification – Support Vector Regression.

Unit IV

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Unit V

Neural Networks
Multi Layer Perceptron – Backpropagation Learning Algorithm – Neural Network fundamentals -Activation functions – Types of Loss Function – Optimization: Gradient Descent Algorithm -Stochastic Gradient Descent – Batch Normalization and Dropouts – Applications of Neural Network.

Course Outcome:

For the complete syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
.

Text Books:

1. Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, An Introduction to Statistical Learning with Applications in R, Springer Texts in Statistics,2013.
2. Thomas A. Runkler, Data Analytics: Models and Algorithms for Intelligent Data Analysis, Springer Vieweg, 2nd Edition,2016.

Reference Books:

1. Jiawei Han, MichelineKamber, Jian Pei, Data Mining: Concepts and Techniques: Concepts and Techniques, Elsevier, 2011.
2. Stephen Marsland, Machine Learning: An Algorithmic Perspective
3. Ian H. Witten, Eibe Frank, Mark A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, Elsevier, 2011
4. J. Tax, Classification, Parameter Estimation and State Estimation: An Engineering Approach Using MATLAB, John Wiley and Sons, 2005.

For detailed syllabus of all the other subjects of Electronics & Instrumentation Engineering 8th Sem, visit EIE 8th Sem subject syllabuses for 2019 regulation.

For all Electronics & Instrumentation Engineering results, visit Anna University EIE all semester results direct link.