DA 203-O Introduction to Computing for AI & Machine Learning 3:1 (January 2023)

Course Instructor: Sashikumaar Ganesan, CDS

Course description:This four-credit course will be offered every year in the August-December term as an elective course to M.Tech. (Online) programme. This course is aimed to be an introductory graduate-level (200-series) course.

This course is aimed at building the foundation of computational thinking with applications to Artificial Intelligence and Machine learning (AI & ML). Besides, how to build a neural network and how to train, evaluate and optimize it with TensorFlow will also be covered in this course.

Syllabus

Programming Foundation: Digital storage of data in computers, memory and data representation, Overflow and Underflow, Round-off errors, the performance of a computer, Caches, Debugging and Profiling, Basic optimization techniques for serial code.

Introduction to Python: Object and Data Structure Basics, Python Statements, Methods and Functions, Object-oriented programming (OOP): Inheritance, Encapsulation, Abstraction, Polymorphism. OOP concepts in Python.

Python tools for Data Science: Pandas, NumPy, Matplotlib, Scikit-Learn, Just-in-Time (JIT) compilers, Numba Computational Thinking: Arrays, Matrix-Vector, Matrix multiplication, Solving dense and sparse systems. Basic machine learning algorithms. Linear Regression, Linear Classification, Multilayer Perceptron, Backpropagation, Automatic Differentiation, Convolutional Networks.

Deep Learning with TensorFlow: Tensors, Install TensorFlow, TensorFlow basics, Simple statistics and plotting, Loading and exploring data, Learning with TensorFlow and Keras, Mini-project.

Textbooks / References

  1. John Hennessy David Patterson. Computer Architecture. A Quantitative Approach. 6th edition, Morgan Kauffman, 2017. https://www.elsevier.com/books/computer-architecture/hennessy/ 978-0-12-811905-1
  2. Shaw, Zed A. Learn python 3 the hard way: A very simple introduction to the terrifyingly beautiful world of computers and code. Addison-Wesley Professional, 2017.
  3. Aurélien Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition, O'Reilly Media, Inc. 2019

Prerequisites: Basic knowledge of mathematics, data structures, and algorithms

Grading:

  • Homeworks 20%
  • Midterm 30%
  • Final Project 20%
  • Final exam 30%.