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Course Modules

Module 1: AI Foundations (Weeks 1–3)

  1. Agents, search, optimization.

  2. Regression, classification, clustering.

  3. Evaluation metrics.

Hands-on: Spam detection, clustering news article

Module 2: Deep Learning Core (Weeks 4–6)

  1. Neural networks, backpropagation.

  2. CNNs (vision), RNNs/LSTMs (sequence).

  3. PyTorch / TensorFlow basics.

Hands-on: MNIST with CNN, text sentiment with LSTM.

Module 3: Transformers & LLM Basics (Weeks 7–9)

  1. Word embeddings (Word2Vec, GloVe).

  2. Transformer architecture: Self-attention, encoder/decoder.

  3. Pre-trained models: BERT, GPT, T5.

  4. Hugging Face transformers library.

Hands-on: Use BERT for text classification, GPT-2 for text generation.

Module 4: OpenAI, Prompt Engineering & APIs (Weeks 10–11)

  1. OpenAI API (ChatGPT, GPT-4, GPT-4o).

  2. Prompt engineering (zero-shot, few-shot, CoT).

  3. Function calling & structured outputs.

  4. System vs user prompts.

Hands-on: Build a Q&A chatbot with OpenAI API.

Module 5: Fine-Tuning & Customization (Weeks 12–14)

  1. Fine-tuning vs. instruction tuning vs. parameter-efficient tuning (LoRA, PEFT).

  2. Hugging Face Trainer API.

  3. Dataset preparation for fine-tuning.

  4. Evaluation of fine-tuned models.

Hands-on: Fine-tune a small GPT-2 on a custom dataset (e.g., legal, medical, or finance).

Module 6: Retrieval Augmented Generation (RAG) & LangChain (Weeks 15–16)

  1. RAG concept: why LLMs need external knowledge.

  2. Vector databases (FAISS, Pinecone, Weaviate).

  3. LangChain basics: chains, agents, tools.

  4. Combining search + LLMs for enterprise apps.

Hands-on: Build a document Q&A bot (upload PDFs, retrieve answers with OpenAI/Hugging Face).

Module 7: Advanced Topics & Responsible AI (Weeks 17–19)

  1. Scaling laws of LLMs.

  2. Multimodal models (GPT-4o, CLIP, Flamingo).

  3. AI safety, fairness, and bias.

  4. Explainability (SHAP, LIME for ML; attribution for LLMs).

Hands-on: Bias detection in LLM responses.

Module 8: Capstone Project (Weeks 20–24)

Students choose one:

  1. LLM Chatbot: domain-specific chatbot (e.g., healthcare/finance) using RAG + fine-tuning.

  2. Generative AI App: story/poetry/image generation with OpenAI APIs.

  3. AI Agent: multi-step reasoning with LangChain agents + tools.

  4. Research Mini-Paper: survey + experiment on fine-tuning techniques.

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