Applied Machine Learning is the subject of the B.E Instrumentation and Control Engineering Four Year program has a semester VII syllabus. In this article, we would like to discuss the unit-wise syllabus.
We are glad to provide the following syllabus from the student’s point of view. We add the required textbooks and references for students to get all the information regarding the syllabus in one place. It will also assist in gaining knowledge before everyone else as a quick referral. We do not leave a topic regarding the syllabus. Hope this information is useful. Kindly share it with your friends. Comment below for any queries about the Subject code EI3752 – Applied Machine Learning Syllabus.
If you want to know more about the B.E Instrumentation and Control Engineering Syllabus connected to an affiliated institution’s four-year undergraduate degree program. We provide you with a detailed Year-wise, semester-wise, and Subject-wise syllabus in the following link B.E Instrumentation and Control Engineering Syllabus Regulation 2021 Anna University.
Aim Of Objectives:
- To give an introduction on several fundamental concepts and methods for machine learning.
- To familiarize with some basic learning algorithms and techniques and their applications.
- To provide the knowledge related to processing, analyzing and handling data sets.
- To illustrate the typical applications of various clustering-based learning algorithms.
EI3752 – Applied Machine Learning Syllabus
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 Overfitting 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: Supervised Learning
Linearly separable and nonlinearly separable populations – 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: 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.
Text Books:
- Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, An Introduction to Statistical Learning with Applications in R, Springer Texts in Statistics, 2013.
- Thomas A. Runkler, Data Analytics: Models and Algorithms for Intelligent Data Analysis, Springer Vieweg, 2nd Edition, 2016.
References:
- EthemAlpaydin, ― Introduction to Machine Learning (Adaptive Computation and Machine Learning), The MIT Press 2004.
- Stephen Marsland, ― Machine Learning: An Algorithmic Perspective, CRC Press, 2009.
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