Statistical Learning Theory Courses and Certifications

MITCBMM

Towards Understanding the Implicit Regularization Effect of SGD

Explore the implicit regularization effect of Stochastic Gradient Descent, uncovering its impact on optimization and generalization in machine learning models.

• YouTube
• 1 hour 14 minutes
• On-Demand
• Free Video

MITCBMM

Open Problems in the Theory of Deep Learning - Panel Discussion

Explore cutting-edge perspectives on deep learning theory with leading experts, addressing unresolved questions and future research directions in this dynamic field.

• YouTube
• 1 hour 2 minutes
• On-Demand
• Free Video

Broad Institute

The Challenges of Training Infinitely Large Neural Networks

Explore challenges in training infinitely large neural networks with Mikhail Belkin. Gain insights into foundational advances in machine learning for biomedical applications.

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

Harvard CMSA

Theory of Machine Learning

Explore fundamental mathematical principles underlying machine learning, neural networks, and deep learning architectures with Princeton researcher Boris Hanin's comprehensive theoretical analysis.

• YouTube
• 1 hour 25 minutes
• On-Demand
• Free Video

Institut Henri Poincaré

Structured Regularization Summer School 2017

Dive into advanced mathematical techniques for structured regularization through expert lectures covering optimization, machine learning, and signal processing applications.

• YouTube
• 22 hours 32 minutes
• On-Demand
• Free Video

Paul G. Allen School

Algorithmic Foundations of Learning and Control - 2019

Explore algorithmic foundations connecting machine learning and control theory through expert lectures from leading researchers in optimization, reinforcement learning, and robotics.

• YouTube
• 19 hours 31 minutes
• On-Demand
• Free Video

Institute for Pure & Applied Mathematics (IPAM)

Learning Theory and Computational Microscopy - IPAM at UCLA

1 review

Explore learning theory applications in computational microscopy, focusing on advanced techniques for image analysis and reconstruction in scientific research.

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

Generalization Theory - Overparameterization, Double Descent, and Inductive Biases - Lecture 6

Explore overparameterization, double descent, and VC dimension limitations while understanding how inductive biases shape deep learning generalization.

• YouTube
• 1 hour 21 minutes
• On-Demand
• Free Video

International Centre for Theoretical Sciences

Learning from Dependent Data - Lecture 2

Explores techniques for analyzing and learning from dependent data, focusing on probabilistic and optimization methods in data science.

• YouTube
• 1 hour 2 minutes
• On-Demand
• Free Video

Institute for Pure & Applied Mathematics (IPAM)

Quantum Statistical Query Learning II of II - IPAM at UCLA

Explores quantum statistical query learning, comparing it to quantum PAC models. Discusses lower bounds, separation results, and potential applications in quantum learning theory.

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

International Centre for Theoretical Sciences

Learning from Dependent Data - Lecture 1

Explore techniques for learning from dependent data, focusing on probabilistic and optimization methods in data science. Gain insights into handling interconnected information effectively.

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

Alan Turing Institute

Measuring the Leakage of a Black-Box Using Machine Learning - Giovanni Cherubin

Explore machine learning techniques for measuring information leakage in black-box systems, focusing on privacy and anonymity protocols in website fingerprinting attacks.

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

MITCBMM

Interpolation and Learning With Scale Dependent Kernels

Explore scale-dependent kernels in machine learning, covering overfitting, statistical theory, and practical applications in regression and interpolation.

• YouTube
• 1 hour 4 minutes
• On-Demand
• Free Video

International Centre for Theoretical Sciences

Learning from Dependent Data - Lecture 1

Explore techniques for learning from dependent data, focusing on probabilistic and optimization methods in data science. Gain insights into handling interconnected information effectively.

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

Simons Institute

Reconsidering Overfitting in the Age of Overparameterized Models

Explore modern machine learning paradigms, focusing on overparameterized models' ability to generalize well despite near-zero training error, challenging classical overfitting concepts.

• YouTube
• 1 hour 18 minutes
• On-Demand
• Free Video