Basic Course

• Basics of Python

• Basics of Deep Learning Library: PyTorch

• Rudiments of Probability Theory for Machine Learning

• Essentials of Matrix Calculus and Linear Algebra for Machine Learning

• Bird's Eye View of Machine Learning

• Primer on Text, Video, and Image Data Processing

Advanced Course
(Emphasizing on Hands-on & Real-World Applications)

• Gradient-based Optimization Techniques

• Rudiments of Artificial Neural Networks and Backpropagation of Error

• Tree-based Classifiers and Ensemble Techniques

• Steps towards Deep Learning: Activation Functions, Normalization techniques, Regularization methods, and loss functions

• Convolutional Neural Networks (CNNs)

• Architectures of Deep Neural Network Models

• Deep Generative Models

• Recurrent Neural Networks and Backpropagation through Time

• Attention Mechanism and Transformers

• Introduction to Graph Neural Networks (GNNs)

• Explainable Artificial Intelligence (XAI)

• Deep Clustering techniques

• Emerging Learning Strategies: Semi-supervised, Few-shot, and Zero-shot

• Adversarial Attacks, Defence, and Robust Deep Neural Network Models

• Theory of Deep Learning

• Deep Reinforcement Learning

• Diffusion-based Models

• Real-world Applications: From Problem to a Solution

  • • • Medical Image Analysis

      • Image segmentation: The path less traveled

      • Text Classification

      • Class Imbalanced Learning

      • Sports Analytics

      • Business analytics and time-series forecasting.

      • Graph data analysis.

      • Federated Learning