Harvard CMSA Courses

Topics in Deep Learning Theory - September 19, 2024

Dive into advanced theoretical concepts of deep learning, exploring mathematical foundations and cutting-edge developments in neural network architecture and optimization techniques.

• YouTube
• 1 hour 16 minutes
• On-Demand
• Free Video

Topics in Deep Learning Theory - October 8, 2024

Dive into advanced theoretical concepts of deep learning through expert-led discussions on neural network architectures, optimization techniques, and mathematical foundations.

• YouTube
• 1 hour 15 minutes
• On-Demand
• Free Video

Graph Theory and Combinatorics

Dive into advanced graph theory and combinatorics concepts, exploring their applications in machine learning through expert-led mathematical analysis and problem-solving techniques.

• YouTube
• 51 minutes
• On-Demand
• Free Video

Enumerative Geometry and Modularity in Two-Modulus K3-Fibered Calabi-Yau Threefolds

Delve into advanced algebraic geometry concepts focusing on K3-fibered Calabi-Yau threefolds, exploring their classification, moduli spaces, and mirror symmetry principles.

• YouTube
• 58 minutes
• On-Demand
• Free Video

Math and Machine Learning Program - Graph Theory and Combinatorics

Explore advanced concepts in graph theory and combinatorics through mathematical approaches to machine learning applications.

• YouTube
• 1 hour 13 minutes
• On-Demand
• Free Video

Deep Learning Theory - October 1, 2024

Dive into advanced theoretical concepts of deep learning, exploring mathematical foundations and cutting-edge developments in neural network architecture and optimization techniques.

• YouTube
• 1 hour 23 minutes
• On-Demand
• Free Video

Hierarchical Data Structures Through the Lenses of Diffusion Models

Explore how diffusion models reveal hierarchical data structures, examining feature behaviors during noising-denoising processes and their implications for understanding generative AI.

• YouTube
• 53 minutes
• On-Demand
• Free Video

Infinite Limits and Scaling Laws for Deep Neural Networks

Delve into the mathematical foundations of neural network scaling, exploring infinite parameter limits, learning dynamics, and how model size and training resources impact deep learning performance.

• YouTube
• 1 hour 6 minutes
• On-Demand
• Free Video

Topics in Deep Learning Theory - September 24, 2024

Explore advanced theoretical concepts in deep learning through expert analysis of foundational principles, mathematical frameworks, and cutting-edge research developments.

• YouTube
• 1 hour 13 minutes
• On-Demand
• Free Video

Introduction to Knot Theory - September 23, 2024

Dive into the fascinating world of knot theory and its mathematical foundations, exploring topological concepts and their applications in modern mathematical analysis.

• YouTube
• 1 hour 27 minutes
• On-Demand
• Free Video

Deep Learning Theory - September 17, 2024

Explore advanced theoretical concepts and foundational principles of deep learning through expert-led discussions and mathematical frameworks.

• YouTube
• 1 hour 5 minutes
• On-Demand
• Free Video

Deep Learning Theory - September 10, 2024

Dive into advanced theoretical concepts of deep learning, exploring mathematical foundations and cutting-edge developments in neural network architecture and optimization techniques.

• YouTube
• 1 hour 9 minutes
• On-Demand
• Free Video

Scaling Limits of Neural Networks

Delve into advanced mathematical analysis of neural networks, exploring scaling limits, structural parameters, and their impact on learning models through theoretical insights and practical applications.

• YouTube
• 57 minutes
• On-Demand
• Free Video

Foundation Models for Real-Time Cancer Diagnosis

Explore groundbreaking AI applications in cancer diagnosis, focusing on microscopic imaging, foundation models, and the integration of cell morphology with molecular profiles for enhanced pathology evaluation.

• YouTube
• 49 minutes
• On-Demand
• Free Video

Data and Model Geometry in Deep Learning - Implications of Geometric Structure

Explore geometric structures in machine learning, focusing on symmetries in data domains and their impact on neural network design, stability, and learnability through unitary group convolutions.

• YouTube
• 55 minutes
• On-Demand
• Free Video