Module 0: Bridge Module
- Python Programming
- Math topics
- Review of basic Statistics
- Review of basic Probability
- Review of basic Calculus
- Review of basic Linear Algebra
Module 1: Preparatory Module Mathematical Preliminaries and basics of Programming
- Linear Algebra
- Probability and Statistics
- Numerical Optimization for ML
- Data Visualization
- Scalable Programming using PySpark
Module 2: Paradigms of Machine Learning Introduction to Learning Paradigms
- Supervised Learning(SL)
- Unsupervised Learning(UL)
Module 3: Deep Learning and its applications Introduction to Deep Learning
- Deep Learning Architectures
- Deep Learning for Speech and Audio Processing
- Deep Learning for Computer Vision
- Deep Learning for Natural Language Processing
- Deep Reinforcement Learning
- Deep Learning for IoT/Edge Devices
Lab/Demo session: (Prerequisite: Basic C and Python.)
- Designing neural network hardware architecture in high level language like C orC++ and port the design on FPGA.
- Various pruning and quantization techniques for optimizing neural network hardware.
- Integrate FPGA and ARM processor on the development board using blockdesign techniques (AXI interfaces) to interface the external sensors.
- Programming and verifying the designed hardware using python API. (All modules except Deep Learning Architectures assume at least 4 hours of Problem solving session)
Module 4: Deploying AI systems
- BigData Platforms and Applications on the Cloud
- Secure Systems
Module 5: Capstone Projects