Course Outcome
PO1
PO2
PO3
PO4
PO5
PO6
PO7
PO8
PO9
PO10
PO11
PO12
3. analyze and design various machine learning based applications with a modern outlook focusing on recent advances. 4. apply some state-of-the-art development frameworks and software libraries for implementation 5. choose the relevant algorithms to perform analytics on real world data. 6. select the best visualization design which can communicate better about the data. Module 1: Introduction to Machine Learning and Data Science (9 hrs) Introduction-Terminology- Data Science Process- Data Science Toolkit- Types of Data- Example Applications-Definition - Types of Machine Learning - Examples of Machine Learning Problems Training versus Testing - Characteristics of Machine Learning Tasks - Predictive and Descriptive Tasks - Machine Learning Models: Geometric Models, Logical Models, Probabilistic Models. Features: Feature types - Feature Construction and Transformation - Feature Selection. Module 2: Data Collection and Management (7 hrs) Introduction, Sources of Data, Data collection and APIs, Exploring and Fixing data, Data storage and Managementusing Multiple Data Sources Module 3: Data Analysis - Classification and Clustering (9 hrs) Classification: Binary Classification- Assessing Classification Performance - Class Probability Estimation - Multiclass Classification - Regression: Assessing performance of Regression - Error Measures - Overfitting-Distance Based Models: Neighbors and Examples - Nearest Neighbors Classification - Distance Based Clustering - K-Means Algorithm - K-Medoids Algorithm - Hierarchical clustering Module 4: Data Analysis - Rule Based and Tree Based Models (8 hrs) Rule Based Models: Rule Learning for Subgroup Discovery - Association Rule Mining - Tree Based Models: Decision Trees - Ranking and Probability estimation Trees - Regression trees - Classification and Regression Trees (CART) Module 5:Data Visualization (7 hrs) Introduction, Types of Data Visualization, Data for Visualization: Data Types, Data Encodings, Retinal Variables, Mapping Variables to Encodings, Visual Encodings. Module 6: Applications (7 hrs) Recent Applications of Data Science and Machine Learning Methods for Machines - Ten Phenomenal Resources for Open Data - Free Data Science Tools and Applications. Text Books: 1. Trevor Hastie, Robert Tibshirani, Jerome Friedman, “The Elements of Statistical Learning: Data Mining, Inference, and Prediction”, Second Edition (Springer Series in Statistics), 2016, ISBN10: 0387848576, ISBN-13: 978-0387848570 2. Cathy O’Neil and Rachel Schutt, “Doing Data Science, Straight Talk from The Frontline”. O’Reilly, 2014. ISBN: 978-1-449-35865-5 3. P. Flach, “Machine Learning: The art and science of algorithms that make sense of data”,Cambridge University Press, 2012, ISBN-10: 1107422221, ISBN-13: 978-1107422223. Reference Books: 1. Joel Grus, “Data Science from Scratch”, O’Reilly, 2015, ISBN: 978-1-491-90142-7 3. 2. Jure Leskovek, Anand Rajaraman and Jeffrey Ullman, “Mining of Massive Datasets. v2.1”, Cambridge University Press, 2014. ISBN: 9781139924801 4 3. Christopher Bishop, “Pattern Recognition and Machine Learning (Information Science and Statistics)”, Springer, 2007. 4. Kevin Murphy, “Machine Learning: A Probabilistic Perspective”, MIT Press, 2012, ISBN10: 0262018020, ISBN-13: 978-0262018029 Course Articulation Matrix:
CO1 CO2 CO3 CO4
3 2 2 2
3
2 2 2
2 -
2 2 3
-
-
-
-
-
-
1
1 3
COMPUTER SCIENCE AND ENGINEERING (2020)
