Generative AI Fundamentals

Learning Objectives

By the end of this 6-month 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 ethical concerns
• Stay informed about the rapidly evolving field of generative AI

 

 

Month 1: Foundations of AI and Machine Learning

Week 1-2: Introduction to Artificial Intelligence

Topics:

• History and evolution of artificial intelligence
• Types of AI: narrow vs. general intelligence
• Machine learning paradigms (supervised, unsupervised, reinforcement)
• AI ethics introduction: bias, fairness, and transparency

Practical Work:

Setting up Python environment

• First simple ML model implementation
• Ethical considerations discussion

Week 3-4: Deep Learning Fundamentals

Topics:

• Neural network basics and architectures
• Forward and backward propagation
• Activation functions and their purposes
• Loss functions and optimization techniques

Practical Work:

• Building a simple neural network from scratch
  • Training models on basic datasets (MNIST, etc.)
  • Implementing with frameworks (PyTorch/TensorFlow basics)

 

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 models
• Evaluation metrics for generated outputs
• Practical Work:
• Working with embeddings and representations
• Visualizing latent spaces
• Basic generative implementations

Week 3-4: Early Generative Architectures

Topics:

• Autoencoders and their variations
• Variational Autoencoders (VAEs)
• Generative Adversarial Networks (GANs)
• Mode collapse and challenges in training

Practical Work:

• Implementing a basic autoencoder
• Training a simple GAN on image data
• Troubleshooting common training issue

Month 2 Assessment:

• Quiz on fundamental concepts

Assignment: Implement and train a VAE or GAN on a simple dataset

 

Month 3: Natural Language Processing and Language Models

Week 1-2: NLP Fundamentals

Topics:

• Text preprocessing techniques
• Word embeddings (Word2Vec, GloVe)
• Recurrent Neural Networks (RNNs)
• Long Short-Term Memory (LSTM) networks
• Practical Work:
• Text preprocessing pipeline
• Training word embeddings
• Building a simple language model

Week 3-4: The Transformer Revolution

Topics:

• Attention mechanisms explained
• The transformer architecture in detail
• Self-attention and multi-head attention
• Positional encoding

Practical Work:

• Implementing attention mechanisms
• Working with transformer blocks
• Basic text generation with transformers

Month 3 Assessment:

• Quiz on NLP and transformer concepts

Assignment: Implement a simple text generator using transformers

 

Month 4: Large Language Models and Applications

Week 1-2: Modern Large Language Models

Topics:

• The evolution of LLMs (GPT series, BERT, T5, etc.)
• Architecture scaling and emergent abilities
• In-context learning and few-shot learning
• Prompt engineering fundamentals

Practical Work:

Using Hugging Face transformers library

• Exploring pre-trained models
• Prompt engineering workshop

Week 3-4: Fine-tuning and Optimization

Topics:

• Transfer learning concepts
• Fine-tuning pre-trained models
• Parameter-efficient fine-tuning (LoRA, adapters)
• Quantization and model optimization

Practical Work:

• Fine-tuning a language model
• Implementing LoRA for efficient adaptation
• Evaluating model performance

Month 4 Assessment:

• Practical assignment: Fine-tune a pre-trained model for a specific task
• Project proposal for final course project

 

Month 5: Multimodal Generative AI

Week 1-2: Image Generation

Topics:

• Evolution of image generation (GANs to diffusion)
• Diffusion models explained
• Text-to-image generation
• Controlling generated outputs

Practical Work:

• Using Stable Diffusion
• Implementing basic diffusion concepts
• Prompt engineering for images

 

 

 

 

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

Topics:

• Speech synthesis and generation

 

• Music generation techniques
  • Video generation fundamentals
  • Multimodal systems architecture
  • Practical Work:
  • Experimenting with audio generation
  • Using multimodal models
  • Creating a simple multimodal application

Month 5 Assessment:

• Mid-term project: Create a multimodal generative application
• Progress report on final project

 

Month 6: Applied Projects and Ethical Considerations

Week 1-2: Building Production Applications

Topics:

• API integration with generative models
• Deployment options and considerations
• Cost optimization and efficiency
• Monitoring and evaluation in production

Practical Work:

• Building a full-stack AI application
• Deploying models to production
• Optimizing for cost and performance

Week 3-4: Ethics, Safety, and Future Directions

Topics:

• Responsible AI development practices
• Bias and fairness in generative systems
• Safety measures and alignment
• Latest research and future directions

Practical Work:

• Evaluating models for bias and fairness
• Implementing safety measures

Final project completion

Final Assessment:

• Comprehensive exam covering all course material
• Final project presentation and demonstration
• Peer review session

 

Resources and Tools

Essential Software and Platforms:

• Python 3.8+ with Jupyter notebooks
• PyTorch or TensorFlow/Keras
• Hugging Face Transformers
• Google Colab or other cloud compute platform
• GitHub for code management

 

Grading and Assessment

Continuous Assessment (70%):

• Weekly quizzes (10%)
• Monthly practical assignments (30%)
• Mid-term project (15%)
• Class participation and discussions (15%)

Final Assessment (30%):

• Final project (20%)
• Comprehensive exam (10%)

 

Study Tips and Expectations

Time Commitment:

• 15-20 hours per week including:
• 2 hours of lecture/theory
• 8-10 hours of practical work
• 4-6 hours of reading and self-study

Recommended Approach:

• Complete all practical exercises
• Form study groups for collaborative learning
• Build a portfolio of projects throughout the course
• Stay updated with the latest research through paper reading
• Participate actively in discussions and peer reviews

 

Final Project Guidelines

Students will develop an end-to-end generative AI application that:

• Solves a real-world problem
• Demonstrates understanding of core concepts
• Implements appropriate ethical safeguards
• Shows technical proficiency with generative models
• Is well-documented and presentable

Projects should include:

• Problem statement and solution approach
• Model architecture and implementation details
• Evaluation methodology and results
• Discussion of limitations and future improvements
• Code repository with clear documentation