Course

Unit 1

Introduction to Machine Learning Overview of intelligent systems and machine learning – Knowledge Discovery process – Data understanding and Data exploration ? Data Preprocessing

Unit 2

Supervised Learning Supervised Learning: Classification introduction, performance evaluation, a first simple classifier: Decision tree ? Rule-based algorithms ? Linear regression ? Logistic regression – Advanced Classification methods: Random Forest, Support Vector Machine, Neural Networks

Unit 3

Unsupervised Learning Unsupervised learning: Clustering: K-Means clustering, DBSCAN – Hierarchical clustering – Pattern mining: a-priori pattern mining

Unit 4

Application of Deep Learning in Bioinformatics Supervised Learning: Deep Learning with Recurrent Neural Networks: architecture – Protein structure/function prediction using machine learning – application of graph neural network for the prediction of protein interaction network – Deep learning applications to genomics :DNA motif discovery – Deep learning applications to genomics: single cell RNAseq analysis and interpretation

Unit 5

Deep Learning Case Study Bioinformatics Define Project Objective – Acquire & Explore Data – Model Building – Model validation – Interpret & Communicate – Data Visualization

REFERENCES: 1. A Silberschatz, H.F. Korth & S. Sudarshan: Data Base System Concepts, TMH, 1997. 2. A.K. Majumdar and Bhattacharyya: Database Management Systems, THM, 1996. 3. C.J. Date: An Introduction to Database systems 7th Ed. Addison Wesley, Indian Edition, 2000.