EI3752: Applied Machine learning syllabus for ICE 2021 regulation

Applied Machine learning detailed syllabus for Instrumentation & Control Engineering (ICE) for 2021 regulation curriculum has been taken from the Anna University official website and presented for the ICE 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 Instrumentation & Control Engineering 7th Sem scheme and its subjects, do visit ICE 7th Sem 2021 regulation scheme. The detailed syllabus of applied machine learning is as follows.

Course Objectives:

Download the iStudy App for all syllabus and other updates.

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

DATA PREPROCESSING
Data quality – Data preprocessing: – Data Cleaning:- Handling missing data and noisy data – Data integration:- Redundancy and correlation analysis – Continuous and Categorical Variables – Data Reduction:- Dimensionality reduction (Linear Discriminant Analysis – Principal Components Analysis).

Unit III

Download the iStudy App for all syllabus and other updates.

Unit IV

CLUSTERING AND UNSUPERVISED LEARNING
Introduction – Clustering:- Partitioning Methods:- K-means algorithm – Mean Shift Clustering -Hierarchical clustering – Clustering using Gaussian Mixture Models – Clustering High-Dimensional Data:- Problems – Challenges

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 – one case study.

Skill Development Activities

(Group Seminar/Mini Project/Assignment/Content Preparation / Quiz/ Surprise Test / Solving GATE questions/ etc) 10

1. Explore the areas and applications where machine learning is used.
2. Collect data for any application and apply data preprocessing techniques.
3. Develop prediction model using the Machine learning techniques.
4. Design controller using Neural Network for any one application

Course Outcomes:

Download the iStudy App for all syllabus and other updates.

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. EthemAlpaydin, �Introduction to Machine Learning (AdaptiveComputation andMachine Learning), The MIT Press 2004.
2. Stephen Marsland, �Machine Learning: An Algorithmic Perspective, CRC Press, 2009

List of Open Source Software/ Learning Website:

1. https://lecturenotes.in/materials/64801-machine-learning-for-engineering-and-science-applications
2. https://nptel.ac.in/courses/106105152
3. https://nptel.ac.in/courses/106106139
4. https://nptel.ac.in/courses/106106202
5. https://nptel.ac.in/courses/110101145

For detailed syllabus of all other subjects of Instrumentation & Control Engineering, 2021 regulation curriculum do visit ICE 7th Sem subject syllabuses for 2021 regulation.

For all Instrumentation & Control Engineering results, visit Anna University ICE all semester results direct link.