Welcome to my BigThinkAI workshops repository!!
This repo contains a collection of AI/ML workshops I've helped create and lead as the AI Lead for BigTh!nkAI @ UMD.
Our goal is to make artificial intelligence accessible — whether you're just starting out or diving into cutting-edge topics. These workshops span beginner to advanced levels and are structured with Colab notebooks, slides, and interactive demos.
And so I hope this repo serves as another way for people to access our resources and learn about our club, and also showcase some of the cool workshops I've helped run!
These are part of our weekly sessions at UMD, designed to build understanding progressively.
-
Intro to AI/ML
Supervised vs. Unsupervised Learning, Data Cleaning with Pandas & NumPy, Linear and Logistic Regression -
Neural Networks (MNIST)
Building from scratch: Dense Neural Networks (128 & 2048 units), Activation Functions, and an intro to Convolutional Neural Networks (CNNs) -
SQL for AI/ML
SQL Basics, Schema Design, Joins and Subqueries, and Real-world Applications in Data-driven AI
These are self-contained, one-off sessions taught during UMD’s annual hackathon, Bitcamp.
-
Reinforcement Learning
Reinforcement Learning basics, Agents, Rewards, Group Relative Policy Optimization (GRPO), GRPO vs PPO, KL Divergence, LoRA & Unsloth integration -
RAG for AI Research
Retrieval-Augmented Generation, RAG pipelines, Vector Embeddings with SentenceTransformers, Vector DBs (Milvus), and LangChain tools for research augmentation
Each folder contains:
- A Jupyter/Colab Notebook (
.ipynb) to follow along - A PPT presentation with key concepts
- A
README.mdthat summarizes the workshop content