ED5340 - Data Science: Theory and practice

Class schedule for Jan-May2024 Semester (Theory in G-slot, Lab in R-slot, Room No. 207, ED building)

NOTE: I class starts at 2.00 pm on 17th Jan 2024 (Wednesday)
Click Download Lecture Slides to download course lecture slides.
Codes used in the class are available at this github page

General Instructions: Come with your laptop (fully charged) and with Python installed (either Anaconda Spyder or Jupiter notebook)

Objectives: The course will equip the students in both theory and practice (through lab sessions) of a few topics in data science

Course Contents:
Python basics, Strings, Lists, Tuples, Sets, Dictionaries, Functions, Classes and objects, File input/output.
Fundamentals of Optimisation: Single-variable and multi-variable optimisation - Optimality Criteria, Non-gradient and Gradient-based methods in single variable, Contour plots, Unidirectional search, Gradient-based approaches in multi-variable, Constrained Optimisation, Implementation of Optimisation.
Machine Learning (ML): Brief introduction, Linear / Polynomial Regression, Logistic Regression (Classification), Regularization, Support vector machines, Clustering, Dimensionality reduction, Manifold learning, 2D/3D Convolution, Introduction to Neural Networks, Evaluation Metrics.
Introduction to python libraries - Numpy (ndarray, indexing, slicing and other functions), Matplotlib, Pandas, Scikit-Learn, Implementation of ML.

Credits 12:
3 theory hours and one 3-hour lab per week.
Text Books:
1) Let us Python, Yashavant Kanetkar and Aditya Kanetkar, First Edition, 2019, BPB Publications
2) Optimization for Engineering Design, - Algorithms and Examples, Kalyanmoy Deb, Second Edition, 2016, PHI
3) Machine Learning Refined: Foundations, Algorithms, and Applications, Jeremy Watt, Reza Borhani, Aggelos K. Katsaggelos, Cambridge University Press, 2nd Edition, 2020

Reference Books:
1) Learn Python 3 the Hard Way, Zed A. Shaw, First Edition, 2018, Pearson Education Inc.
2) https://www.w3schools.com/python/
3) https://scikit-learn.org/stable/
4) https://matplotlib.org/tutorials/introductory/pyplot.html
5) Data science from scratch - First principles with Python, Joel Grus, OâReily, 2015.
6) Machine Learning, Tom Mitchell, McGrawhill, 1997
7) Introduction to Optimum Design, Jasbir Arora, Academic Press, 2016

Requirements

Installation and downloadables

Typical Grading Policy (subjected to changes depending on the situtation)



Back to Home