Offered Topics in WSDL 24
(Emphasizing on Hands-on & Real-World Applications)

• Basics of Python and Libraries of Importance.

• 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.

• 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.

• Architectures of Deep Neural Network Models.

• Generative Deep Neural Network Models: GANs, VAEs, and Diffusion.

• Recurrent Neural Networks and Backpropagation through Time.

• Attention mechanism and Transformers: Where Text meets Vision.

• Large Language Models, pre-training, task adaptation, and fine-tuning.

• Introduction to Graph Neural Networks.

• Explainable Artificial Intelligence 

• Deep Clustering techniques.

• Emerging Learning strategies: Semi-supervised, few-shot, zero-shot, etc.

• Adversarial Attacks, Defence, and Robust Deep Neural Network models.

• Theory of Deep Learning: Special Focus on GenAI.

• Deep Reinforcement Learning.

• Real-world Applications: From Problem to a Solution

  • • • • Image segmentation and Medical Image Analysis.

        • Natural Language Understanding: Text Classification, question answering, summarization, etc.

        • Working with LLMs: Tips and Tricks, quantization, approximation, RLHF, RLAIF, etc.

        • Prompt engineering.

        • Business analytics and time-series forecasting.

        • Graph data analysis.

        • Deep Embedding Learning Strategies: Contrastive Learning and Sentence Transformers. 

        • Federated Learning

        • Class Imbalanced Learning