Course Information: Intelligent Design Model (CSET225) July-Dec 25

Detailed Syllabus 

Module 1: Why Intelligent Model? AI in Sensitive Applications, AI in Healthcare and Security, Pervasive, AI Systems, AI in IOT Devices, Biases in AI Models, Types of Dataset - 1D Data (Tabular Data, CSV Files, DAT Files, MAT Files, Time Series Data, Spectral Data), 2D Data (RGB, grayscale images), 3D Data (e.g., MRI scans, Multispectral imaging, Hyperspectral Imaging Data), Introduction to Image Data in AI, Introduction to Convolutional Neural Networks (CNN), Need for CNN (Limitations of ANN), CNN Architecture and Components, Convolutional Layers, Pooling Layers, Fully Connected Layers, Activation Functions in CNN, Training CNN: Techniques and Optimization, Feature Extraction in CNN, Understanding Filters and Kernels in CNN, Regularization, Applications of CNN in Image Processing and Beyond, Adversarial Attack. 

Module 2: Explainable AI, Introduction to Text Data in AI, Recurrent Neural Networks (RNN), Architecture and Working of RNN, Challenges with RNN: Vanishing and Exploding Gradients, Applications of RNN in Sequential Data Analysis, Long Short-Term Memory (LSTM), LSTM Architecture: Gates and Memory Cells, Training LSTM Networks, Applications of LSTM, Gated Recurrent Unit (GRU), GRU vs. LSTM: A Comparative Analysis, Applications of GRU in Sequence Modeling. 

Module 3: Introduction to U-Net: Extending CNN for Image Segmentation, U-Net Architecture: Encoder-Decoder Structure, Skip Connections and Their Role in U-Net, Training U-Net for Biomedical Image Segmentation. What is Generative AI? Working of Generative AI models, Types of Generative models, Evaluating Generative AI Models, Introduction to Generative Adversarial Networks (GANs), Architecture of GAN: Generator and Discriminator, Training GANs: Challenges and Techniques, Applications of GANs, Introduction and Architecture of Diffusion models, Introduction and Architecture of Transformers, Introduction and Architecture of Variational Autoencoders. Comparison between each type of Generative models, Introduction to Prompt Engineering, Types of Prompts in AI, Structure of good prompts, Challenges in prompt engineering  

Module 4: Model fine tuning, Transfer Learning, Domain Adaption, Explainable AI: Techniques and Applications, Large Language Models (LLM), Transformers and the Evolution of LLM, Understanding LLM in Text Generation, Training LLMs: Challenges and Strategies, Applications of LLM in Chatbots, Text Summarization, and Translation