23ADM3 - Predictive Machine Learning Algorithms 

Pre-requisite:Probability and Statistics

Course Educational Objective: The objective of the course is to provide the basic concepts and techniques of Machine Learning and help to use recent machine learning approaches for solving practical problems. It enables students to gain experience to do independent study and research.

Course Outcomes:At the end of this course,the student will be able to

CO1:Identify the characteristics of machine learning.(Understand-L2)

CO2:Understand the Model building and evaluation approaches.(Understand-L2) 

CO3: Apply regression algorithms for real-world Problems. (Apply- L3)

CO4:Handle classification problems via supervised learning algorithms.(Apply-L3) 

CO5: Learn advanced learning techniques to deal with complex data. (Apply- L3)

UNITI:Introduction to Machine Learning-Introduction, Types of Machine Learning, Applications of Machine Learning, Issues in Machine Learning. Preparing to Model- Introduction, Machine Learning Activities, Basic Types of Data in Machine Learning, Exploring Structure of Data.

UNITII: Modeling- Introduction, Model Representation and Interpretability, Evaluating Performance of a Model.

Basics of Feature Engineering - Introduction, Feature Transformation – Feature Construction, Feature Extraction, Principal Component Analysis(PCA), Feature Subset Selection

UNIT III: Regression: Introduction to regression analysis, Simple linear regression, Multiple linear regression, Assumptions in RegressionAnalysis,Main Problems in Regression Analysis, Polynomial Regression Model, Logistic Regression, Regularization, Regularized Linear Regression, Regularized Logistic Regression.

UNIT IV: Supervised Learning: Classification- Introduction, Example of Supervised Learning, Classification Model, and Classification Learning Steps. Common ClassificationAlgorithms-k-Nearest Neighbor(kNN),SupportvectorMachines (SVM), Random Forest model.Evaluating Performance of a Model.

UNIT-V:Other Types of Learning: Ensemble Learning-Bagging, Boosting, Stacking and its impacton bias and variance, AdaBoost, Gradient Boosting Machines, XGBoost.

ReinforcementLearning-Introduction, QLearning

Textbooks:

1. SubramanianChandramouli,Saikat Dutt,Amit Kumar Das, “Machine Learning”, Pearson Education India ,1st edition,2015.

2. TomM.Mitchell, “MachineLearning’,MGH, 1997.

 ReferenceBooks:

1. Shai Shalev-Shwartz, ShaiBen David, “Understanding Machine Learning: From Theory to Algorithms”, Cambridge.

2.     PeterHarington,“MachineLearninginAction”,Cengage,1stedition,2012.

3. Peter Flach, “Machine Learning: The art and science of algorithms that make sense of data”, Cambridge university press,2012.

4. Jason Brownlee, “Machine Learning Mastery with Python Understand Your Data,CreateAccurateModelsandWorkProjects End-To-End”, Edition:v1.4, 2011.