Generative AI

A rigorous deep dive into the architectures, mathematics, and code defining the future of Artificial Intelligence.

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Class code: 2pxf2bro
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Course Syllabus

Foundations of AI
4 Lectures

Part I: Foundations

  • Probability Theory & Linear Algebra
  • Optimization & Information Theory
  • Deep Learning Architectures & Attention
  • CLIP, Contrastive Learning & Autoencoders
Generative Art
4 Lectures

Part II: Generative AI for Images

  • VQ-VAE & Generative Adversarial Networks
  • Normalizing Flows & Flow Matching
  • Diffusion Models (DDPM, Score-Based)
  • Diffusion Architectures (LDM, DiT, Adapters)

💻 Hands-on Labs

  • Lab 1: VAE & GAN Implementation
  • Lab 2: Diffusion Models: DDPM Training & Sampling
Natural Language Processing
6 Lectures

Part III: Generative AI for Text

  • NLP Foundations (Tokenization, Embeddings)
  • LLM Architecture (GPT, LLaMA)
  • Training & Scaling Laws
  • Alignment (RLHF, DPO, PEFT)
  • RAG & Efficiency
  • Agents (ReAct, Tool Use, Planning)

💻 Hands-on Labs

  • Lab 3: NanoGPT: Building a Micro-GPT
  • Lab 4: LLM Applications: RAG & Agents
Future of AI
6 Lectures

Part IV: Frontiers & Advanced Topics

  • Multimodal Models (CLIP, LLaVA, GPT-4V)
  • Deep Generative Modeling - Theory & Practice
  • Ethics & The Future
  • Exercises & Comprehensive Review

💻 Hands-on Labs

  • Lab 5: Multimodal: VLM Applications & Fine-tuning

Tentative Schedule

Date Day Type Topic Content
Part I: Foundations (Ch 1–7)
Feb 25 Wed Lecture 01: Foundations I Probability Theory, Linear Algebra.
Feb 27 Fri Lecture 02: Foundations II Optimization, Information Theory.
Mar 04 Wed Lecture 03: Deep Learning & Attention DL Architectures, CNNs, Transformers.
Mar 06 Fri Lecture 04: CLIP & Autoencoders Contrastive Learning, VAEs, ELBO.
Part II: Generative AI for Images (Ch 8–13)
Mar 11 Wed Lecture 05: VQ-VAE & GANs Vector Quantized Models, Adversarial Training.
Mar 13 Fri Lab 01 VAE & GAN Lab VAE implementation, GAN training.
Mar 18 Wed Lecture 06: Normalizing Flows Invertible Networks, Flow Matching.
Mar 20 Fri Lecture 07: Diffusion Models DDPM, Score-Based Models, U-Net.
Mar 25 Wed Lab 02 Diffusion Lab DDPM & Latent Diffusion Training.
Apr 27 Fri Exercise 08: Comprehensive Vision AI Review Exercises on Foundations, VAEs, GANs, Diffusion.
Part III: Generative AI for Text (Ch 14–19)
Apr 01 Wed Lecture 09: NLP Foundations Tokenization, Embeddings, RNNs.
Apr 03 Fri Holiday Easter Break (No Class)
Apr 08 Wed Lecture 10: LLM Architecture GPT, LLaMA, Inference Optimization.
Apr 10 Fri Lecture 11: Training & Scaling Scaling Laws, Data, Distributed Training.
Apr 15 Wed Lab 03 NanoGPT Lab Building a Micro-GPT from Scratch.
Apr 17 Fri Lecture 12: Alignment RLHF, DPO, PEFT.
Apr 22 Wed Lecture 13: RAG & Agents Scaling, Retrieval, ReAct, Tool Use.
Apr 24 Fri Exercise 14: Comprehensive Text AI Review Review of NLP, LLMs, Alignment, RAG.
Apr 29 Wed Lab 04 LLM Applications Lab RAG & Agent Implementation.
Part IV: Frontiers & Advanced Topics (Ch 20–24)
May 01 Fri Holiday Labor Day (No Class)
May 06 Wed Lecture 15: Multimodal Models CLIP, LLaVA, GPT-4V.
May 08 Fri Exercise 16: Deep Generative Modeling - Theory & Practice VAE, GAN, Diffusion, Transformer Exercises.
May 13 Wed Lecture 17: Ethics & The Future Safety, Bias, World Models, AGI.
May 15 Fri Lab 05 Multimodal Lab VLM Applications & Fine-tuning.
May 20 Wed Exercise 18: Exercises I Foundations, Images & Diffusion Review.
May 22 Fri Exercise 19: Exercises II LLMs, RAG & Frontiers Review.
May 27 Wed Lecture 20: TBD To be announced.
Prof. Fabrizio Silvestri

Prof. Fabrizio Silvestri

Course Instructor

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TA

TBD

Teaching Assistant

To Be Defined