teaching_collated

Teaching Materials by Soumya Banerjee

A curated index of public teaching repositories under neelsoumya, with links and brief descriptions. The descriptions are based on repository names, available README material, and repository pages; individual notebooks and linked resources may contain additional material.

Large language models, generative AI, and AI safety

• Teaching LLM Applications — MSc-level course on LLM internals and applications, with lecture notes and Python practicals. Topics include tokenisation, embeddings, attention, Transformers, pre-training and scaling, fine-tuning/RLHF, prompting, RAG, agents and tools, evaluation, safety, interpretability, DPO, efficient architectures, and reinforcement learning.
• Teaching LLM Agents — Teaching resources on LLM-based agents, agent workflows, tool use, and related application patterns.
• AI Safety Teaching Resources — Course resources for introducing AI safety, AI ethics, and philosophy of AI, including a course PDF and further reading.
• Special Topics in Unconventional AI — Special-topics course on unconventional approaches to artificial intelligence, with a companion course website.
• Winter School in AI — AI teaching and practical material, including introductory Transformer notebooks.
• AI Outreach / PPI Resources — Public-facing resources for explaining AI, especially AI in healthcare, to patients and the wider public. Includes interactive explainers, browser-based model-building tools, responsible-AI resources, and material on patient and public involvement.
• Outreach and PPI (original repository) — Earlier repository associated with public engagement and patient/public involvement resources for trustworthy AI.
• AI for Biomedical Students — AI and machine-learning teaching resources aimed at biomedical and life-science students.
• AI for School Students — Introductory AI material for school-age learners.
• Public Talk: AI in India — Material supporting a public-facing talk about AI in the Indian context.
• IITM Generative AI — Generative-AI teaching material associated with IIT Madras.
• Teaching Neural Network Animation — Small repository for teaching neural networks through animation or visual explanation.

Machine learning, deep learning, and neural networks

• Python Machine Learning — Broad Python-based ML teaching collection with notebooks, notes, code, and data. Covers Python basics, supervised and unsupervised learning, PCA, t-SNE, embeddings, feature engineering, neural networks, backpropagation, and applied examples including healthcare data.
• Public Teaching: Unsupervised Learning — Teaching notes and practical resources focused on unsupervised machine learning and dimensionality reduction.
• Teaching Neural Networks — Beginner-friendly neural-network and CNN course, with visual explanations, practical code, and biological applications such as microscopy, cell classification, and medical imaging.
• Introduction to PyTorch — Hands-on introduction to tensors, autograd, forward/backward passes, linear regression, and simple neural networks.
• Butterfly Detector — Collection of deep-learning tutorials and notebooks, including image classification, Fashion-MNIST, variational autoencoders, graph neural networks, reinforcement-learning interfaces, and neural cellular automata.
• Visualization Lecture — Notes, slides, code, and datasets for data visualization, dimensionality reduction, and the mathematics of machine learning. Includes Python exercises and material on communicating data through visual narratives.
• Linear Mixed-Effects Model Teaching — Teaching examples and resources for linear mixed-effects models and statistical modelling.
• Complex Systems Teaching Resources — Repository for teaching material on complex systems.
• Mathematics — Small repository of mathematics-related teaching or learning material; inspect the files for the current contents.

Robotics and navigation

• Teaching Robot Navigation — Robotics course notes and practicals covering ROS/ROS 2, Gazebo/RViz, odometry, localisation, mapping, SLAM, path planning, autonomous navigation, indoor drones, cooperative robots, safety and ethics, and connections between LLMs and robotics. Includes practical examples and course links.
• Mumbai Teaching — Teaching material associated with Mumbai; see the repository for the specific course and resources.
• Wuhan COVID Scavenger Hunt (Streamlit) — Interactive Streamlit-based teaching activity using a scavenger-hunt format around COVID-related information.

Programming, web development, reproducibility, and data science practice

• Teaching Web Development — Introductory full-stack web-development material, including HTML/CSS, PHP, forms, databases, authentication, security practices, labs, and assessment ideas.
• Teaching Reproducible Science in R — Resources for reproducible computational research using R and R Markdown, including documented analysis workflows, exercises, slides, and examples of reproducible projects.
• Teaching R Markdown and Git — Compact collection of resources for teaching R Markdown, Git, and reproducibility, with links to training material and related repositories.
• Getting Started with Data Science — Introductory resources for getting started with data science; check the repository contents for its current lesson structure.
• Teaching Big Data Analytics — Teaching repository for big-data analytics.
• Teaching Resources — General compilation of teaching resources across data science, machine learning, bioinformatics, and biology.
• Theory of Computer Science Teaching — Teaching resources for theoretical computer science.
• Teachable Machine Community — Community-oriented material related to browser-based, accessible machine-learning education.
• Teachable Machine v1 — Code/resources related to an early Teachable Machine project for interactive, browser-based machine learning.

Broader open-source collections with teaching material

• Public Open-Source Data Science — Larger catalogue of open-source data-science projects, including resources for AI for social good, biology, clinical informatics, NLP, LLMs, browser-based data science, and public-facing AI education.
• AI Autograder Project — Project repository exploring AI-supported grading or assessment workflows.
• Working with Domain Experts — Small repository related to interdisciplinary collaboration and working with subject-matter experts; inspect its files for details.

Suggested starting points

For a quick overview of the teaching portfolio, start with:

1. LLM Applications — a structured, advanced course with notes and practicals.
2. Python Machine Learning — broad ML coverage with notebooks and examples.
3. Robot Navigation — robotics, navigation, SLAM, and practicals.
4. AI Safety Teaching Resources — AI safety and ethics.
5. Teaching Neural Networks — accessible deep-learning material with biological examples.
6. Reproducible Science in R — reproducible workflows and research practice.
7. Visualization Lecture — data visualization and dimensionality reduction.
8. AI Outreach — public engagement and trustworthy AI.