Discontinued (Jun 2025)- Bachelor of Science in Programming and Data science 

offered by IIT Madras

Introduction, testing in software development life-cycle, software testing process levels, testing terminology

Techniques and algorithms for test case design

Open source tools and frameworks for testing

Testing of applications: Testing OO-applications, web applications, GUI and mobile applications

Deconstructing the software development process: In this week, we will introduce the stages of software development through an exercise of deconstructing a large and complex software system

Discussions in this week will motivate and provide pointers to parts of the software development process, which we will discuss in subsequent weeks

Software Requirements: In this week, learners will be able to identify different types of requirements

Software Conceptual Design: In this week, learners will learn how to create the software conceptual design from requirements. They will be introduced to the Function-Behaviour-Structure (FBS) framework, a popular framework which helps learners think about software design in terms of its functions, structures and behaviors. There will be hands-on activities in a learning environment, think&link. This will help learners map software design solution parts to the FBS framework, and come up with comprehensive and cohesive software designs

Introduction. History and philosophy. The Turing Test. The Winograd Schema Challenge

State space search. Depth first, breadth First, DFID, comparison

Heuristic search. Heuristic functions. Solution space search. Escaping local optima, Stochastic local search

Population based methods. Genetic algorithms, emergent systems, ant colony optimization

History of Deep Learning, McCulloch Pitts Neuron, Thresholding Logic, Perceptron Learning Algorithm and Convergence

Multilayer Perceptrons (MLPs), Representation Power of MLPs, Sigmoid Neurons, Gradient Descent

Feedforward Neural Networks, Representation Power of Feedforward Neural Networks, Back propagation

Gradient Descent(GD), Momentum Based GD, Nesterov Accelerated GD, Stochastic GD, Adagrad, RMSProp, Adam

Introduction to Design Thinking

Empathize - P1

Empathize - P1

Analyze - P1

Review of Signals and Systems, Continuous time signals and transforms

Discrete time signals, Discrete Fourier transform, Autocorrelation and Cross-Correlation

Acoustic Feature Analysis of Speech Signals I

Acoustic Feature Analysis of Speech Signals II

Datasets

Deep Learning Frameworks

Model training

Hardware for DL

Introduction to the course, notion of thematic ideas, feature engineering

Data imputation, Handling class imbalance

Bootstrapping

Cross-validation