Generative AI Fundamentals

Duration: 6 Months
Mode: Hybrid (Online + Practical Projects)
Level: Intermediate to Advanced

This course provides a comprehensive foundation in Generative Artificial Intelligence (AI) — from basic machine learning principles to building real-world AI-driven applications. Learners will explore core generative models, fine-tuning methods, ethical AI practices, and deployable solutions across text, image, audio, and multimodal domains.

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Course Objectives

By the end of the course, students will be able to:

• Understand core AI and machine learning concepts

• Explain how different generative models function

• Implement basic generative AI systems using Python

• Fine-tune pre-trained models for specific applications

• Design and build end-to-end generative AI applications

• Evaluate generative outputs for quality and ethics

• Stay updated with the rapidly evolving AI landscape

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Course Curriculum

Month 1: Foundations of AI and Machine Learning

Week 1–2: Introduction to Artificial Intelligence

Topics:

• History and evolution of AI

• Types of AI: Narrow vs. General Intelligence

• Machine Learning paradigms (Supervised, Unsupervised, Reinforcement)

• Introduction to AI ethics: Bias, Fairness, Transparency

Practical Work:

• Setting up Python environment

• Implementing your first simple ML model

• Discussion on ethical implications

Week 3–4: Deep Learning Fundamentals

Topics:

• Neural network architectures

• Forward & backward propagation

• Activation, loss, and optimization functions

Practical Work:

• Building a simple neural network from scratch

• Training models on basic datasets (e.g., MNIST)

• Implementing with PyTorch/TensorFlow

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Month 2: Understanding Generative Models

Week 1–2: Fundamentals of Generative Models

Topics:

• Discriminative vs. Generative modeling

• Latent spaces and representations

• Probability distributions in generative systems

• Evaluation metrics for generated outputs

Practical Work:

• Working with embeddings and visualizing latent spaces

• Implementing basic generative techniques

Week 3–4: Early Generative Architectures

Topics:

• Autoencoders and Variational Autoencoders (VAEs)

• Generative Adversarial Networks (GANs)

• Mode collapse and training challenges

Practical Work:

• Implementing autoencoders and GANs

• Training models on image data and troubleshooting

Assessment:

• Quiz on fundamentals

• Assignment: Implement and train a VAE or GAN

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Month 3: Natural Language Processing (NLP) and Language Models

Week 1–2: NLP Fundamentals

Topics:

• Text preprocessing

• Word embeddings (Word2Vec, GloVe)

• RNNs and LSTMs

Practical Work:

• Text preprocessing pipeline

• Training word embeddings

• Building a simple language model

Week 3–4: The Transformer Revolution

Topics:

• Attention mechanisms

• Transformer architecture and positional encoding

• Self-attention and multi-head attention

Practical Work:

• Implementing attention mechanisms

• Working with transformer blocks

• Basic text generation

Assessment:

• Quiz + Assignment: Implement a simple text generator

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Month 4: Large Language Models (LLMs) and Applications

Week 1–2: Modern LLMs

Topics:

• Evolution of LLMs (GPT, BERT, T5, etc.)

• In-context and few-shot learning

• Prompt engineering

Practical Work:

• Using Hugging Face Transformers

• Exploring pre-trained models

• Prompt engineering exercises

Week 3–4: Fine-Tuning and Optimization

Topics:

• Transfer learning and parameter-efficient tuning (LoRA, Adapters)

• Quantization and optimization

Practical Work:

• Fine-tuning LLMs for tasks

• Implementing LoRA

• Evaluating model performance

Assessment:

• Fine-tune a pre-trained model + Project proposal

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Month 5: Multimodal Generative AI

Week 1–2: Image Generation

Topics:

• Evolution of image generation (GANs → Diffusion)

• Diffusion models and text-to-image generation

• Controlling generated outputs

Practical Work:

• Using Stable Diffusion

• Implementing basic diffusion models

• Image prompt engineering

Week 3–4: Audio, Video, and Multimodal Generation

Topics:

• Speech synthesis and music generation

• Video generation

• Multimodal architectures

Practical Work:

• Experimenting with audio and multimodal models

• Creating a simple multimodal application

Assessment:

• Mid-term project: Multimodal generative application

• Progress report on final project

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Month 6: Applied Projects and Ethical AI

Week 1–2: Building Production Applications

Topics:

• API integration

• Deployment and cost optimization

• Monitoring & evaluation in production

Practical Work:

• Building a full-stack AI app

• Deploying and optimizing models

Week 3–4: Ethics, Safety, and Future of AI

Topics:

• Responsible AI practices

• Bias, fairness, and safety

• Latest research and emerging trends

Practical Work:

• Evaluating ethical concerns

• Implementing safety measures

Final Assessment:

• Comprehensive exam

• Final project presentation

• Peer review session

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Resources and Tools

• Languages & Frameworks: Python 3.8+, PyTorch, TensorFlow/Keras

• Platforms: Hugging Face, Google Colab, GitHub

• Tools: Jupyter Notebooks, Stable Diffusion, API deployment tools

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Grading and Evaluation

Continuous Assessment (70%)

• Weekly quizzes – 10%

• Monthly assignments – 30%

• Mid-term project – 15%

• Participation – 15%

Final Assessment (30%)

• Final Project – 20%

• Comprehensive Exam – 10%

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Study Tips and Expectations

Time Commitment: 15–20 hours/week

• 2 hours lectures

• 8–10 hours practical

• 4–6 hours reading/self-study

Recommendations:

• Complete all practicals

• Form study groups

• Build a project portfolio

• Stay updated with latest AI research

• Engage in discussions and peer reviews

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Final Project Guidelines

Students will create a fully functional generative AI application that:

• Addresses a real-world challenge

• Demonstrates technical proficiency

• Includes ethical safeguards

• Is well-documented and reproducible

Project Components:

• Problem statement & solution approach

• Model architecture & implementation

• Evaluation & results

• Limitations & future improvements

• GitHub repository with documentation