Social And Emotional Learning Courses

Centre for Quantum Technologies

Scalable Quantum Machine Learning Models in Fourier Space

Discover how Fourier analysis enables massively scalable quantum machine learning models, training on classical hardware with thousands of qubits and millions of parameters.

• YouTube
• 53 minutes
• Self-Paced
• Free Video

Centre for Quantum Technologies

Interactive Proofs for Verifying Quantum Learning and Testing

Explore interactive proof protocols for quantum learning and testing with resource constraints, examining delegation to untrusted parties and quantum communication advantages.

• YouTube
• 21 minutes
• Self-Paced
• Free Video

Centre for Quantum Technologies

QuantumBoost - A Lazy, Yet Fast, Quantum Algorithm for Learning

Discover QuantumBoost, a quantum algorithm that combines weak learners into strong classifiers with superior runtime complexity, featuring insights from AI-assisted development.

• YouTube
• 48 minutes
• Self-Paced
• Free Video

Centre for Quantum Technologies

Designing Quantum Machine Learning Models for Graphs

Explore geometric quantum machine learning models for graph datasets, understanding their design principles and benefits over classical approaches.

• YouTube
• 19 minutes
• Self-Paced
• Free Video

Centre for Quantum Technologies

A Universal Script for Machine Learning Derived Entanglement Witnesses

Discover a machine learning approach to generating entanglement witnesses with customizable measurement settings for quantum systems up to 5 qubits and various qudit configurations.

• YouTube
• 17 minutes
• Self-Paced
• Free Video

Centre for Quantum Technologies

Learning Pure Quantum States (Almost) Without Regret

Discover quantum state tomography techniques that minimize sample disturbance while achieving maximal precision through sequential measurements with polylogarithmic regret scaling.

• YouTube
• 19 minutes
• Self-Paced
• Free Video

Centre for Quantum Technologies

Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits

Discover efficient learning methods for quantum circuits with bounded gates, exploring sample complexity, kernel-based approaches, and practical applications up to 60 qubits.

• YouTube
• 18 minutes
• Self-Paced
• Free Video

Centre for Quantum Technologies

Kernel-based Dequantization of Variational Quantum Machine Learning

Discover kernel-based methods for dequantizing quantum machine learning models using trigonometric kernels and Random Fourier Features to match quantum circuit performance classically.

• YouTube
• 17 minutes
• Self-Paced
• Free Video

Centre for Networked Intelligence, IISc

Learning with Minimal Human Feedback

Discover algorithms for online classification with minimal human feedback, combining concentration techniques with convex geometry for efficient model alignment and fine-tuning.

• YouTube
• 1 hour 1 minute
• Self-Paced
• Free Video

Simon Scrapes | AI Automation

Master RAG Workflows in N8N by Learning These 4 Nodes

Master professional RAG workflows in N8N by learning 4 essential nodes: agentic chunking, Supabase integration, query expansion, and Cohere reranking for precise AI responses.

• YouTube
• 23 minutes
• Self-Paced
• Free Video

Galileo Galilei Institute (GGI)

Machine Learning for Fundamental Physics - Lecture II

Explore advanced machine learning techniques applied to fundamental physics problems through expert instruction from Jesse Thaler in this comprehensive second lecture.

• YouTube
• 1 hour 38 minutes
• Self-Paced
• Free Video

Galileo Galilei Institute (GGI)

Machine Learning for Fundamental Physics - Lecture I

Discover how machine learning transforms fundamental physics research through advanced computational techniques and theoretical applications.

• YouTube
• 1 hour 37 minutes
• Self-Paced
• Free Video

Centre International de Rencontres Mathématiques

Challenges in Unsupervised Learning - Statistical-Computational Trade-offs - Part 2

Explore statistical-computational trade-offs in unsupervised learning, examining when algorithms can achieve both efficiency and optimality in pattern recognition.

• YouTube
• 1 hour 4 minutes
• Self-Paced
• Free Video

Centre International de Rencontres Mathématiques

Challenges in Unsupervised Learning - Statistical-Computational Trade-offs - Part 1

Explore statistical-computational trade-offs in unsupervised learning, examining when algorithms can achieve both efficiency and optimality in pattern discovery.

• YouTube
• 1 hour 6 minutes
• Self-Paced
• Free Video

Richard McElreath

Statistical Rethinking 2026 - Group-Level Confounding and Intro to Social Networks - Lecture B04

Explore group-level confounding effects and social network analysis fundamentals in statistical modeling and causal inference.

• YouTube
• 59 minutes
• Self-Paced
• Free Video