EI3752 Applied Machine Learning Syllabus:

EI3752 Applied Machine Learning Syllabus – Anna University Regulation 2021

COURSE OBJECTIVES:

1. 1. To give an introduction on several fundamental concepts and methods for machinelearning.
2. To familiarize with some basic learning algorithms and techniques and their applications.
3. To provide the knowledge related to processing, analyzing and handling data sets.
4. To illustrate the typical applications of various clustering based learning algorithms

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 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.

TOTAL 45 PERIODS

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

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:

CO1 Ability to understand the basic theory underlying machine learning.
CO2 Ability to understand a range of machine learning algorithms along with their strengths and weaknesses.
CO3 Ability to formulate machine learning problems corresponding to different applications.
CO4 Ability to apply machine learning algorithms to solve problems of moderate complexity.
CO5 Ability to read current research papers and understand the issues raised by current research.

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.

REFERENCES:

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