Statistical Learning Theory Courses and Certifications

MITCBMM

Statistical Learning Theory and Applications - Fall 2015

Dive into statistical learning theory fundamentals, regularization techniques, kernel methods, and deep learning representations through MIT's comprehensive graduate-level curriculum.

• YouTube
• 1 day 12 hours 53 minutes
• On-Demand
• Free Video

MITCBMM

Statistical Learning Theory and Applications [2019]

Master statistical learning theory fundamentals and practical applications through comprehensive MIT lectures covering key algorithms and mathematical foundations.

• YouTube
• 1 day 7 hours 54 minutes
• On-Demand
• Free Video

MITCBMM

Statistical Learning Theory and Applications - Fall 2018

Dive into statistical learning theory fundamentals and practical applications through MIT's comprehensive lecture series covering key algorithms and mathematical foundations.

• YouTube
• 1 day 8 hours 38 minutes
• On-Demand
• Free Video

MITCBMM

Statistical Learning Theory and Applications

Master statistical learning theory through kernel methods, regularization techniques, and deep learning optimization with rigorous mathematical foundations and practical applications.

• YouTube
• 23 hours 52 minutes
• On-Demand
• Free Video

Simons Institute

Statistical Learning Theory and Neural Networks II

Explores advanced concepts in statistical learning theory and neural networks, focusing on optimization perspectives, gradient descent analysis, and the neural tangent kernel regime.

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

Simons Institute

Statistical Learning Theory and Neural Networks

Explore statistical learning theory for deep neural networks, covering uniform laws, Rademacher complexity, and optimization perspectives including gradient descent and neural tangent kernel regime.

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

MITCBMM

Statistical Learning Theory and Applications - Class 6

Explore statistical learning theory with focus on Newton's method, recursive computation, and online methods for efficient problem-solving and data analysis.

• YouTube
• 1 hour 33 minutes
• On-Demand
• Free Video

MITCBMM

Statistical Learning Theory and Applications - Class 7

Explore statistical learning theory, covering least squares, gradient descent, regularization, and induction, with insights into historical context and practical applications.

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

Institute for Advanced Study

Statistical Learning Theory for Modern Machine Learning - John Shawe-Taylor

Explore statistical learning theory's role in modern machine learning, covering PAC-Bayes framework, risk measures, and deep network training experiments with John Shawe-Taylor.

• YouTube
• 1 hour 12 minutes
• On-Demand
• Free Video

MITCBMM

Statistical Learning Theory and Applications - Class 16

Explore statistical learning theory, focusing on complexity, dual norm equality, and calligraphic G. Gain insights into advanced concepts and their applications in machine learning.

• YouTube
• 1 hour 20 minutes
• On-Demand
• Free Video

Simons Institute

Overview of Statistical Learning Theory - Part 2

Explore fundamental concepts in statistical learning theory, including capacity control, generalization, and optimization techniques. Gain insights into classic theory and modern applications.

• YouTube
• 1 hour 3 minutes
• On-Demand
• Free Video

Simons Institute

Overview of Statistical Learning Theory - Part 1

Explore fundamental concepts of statistical learning theory, including capacity control, generalization, and optimization techniques. Gain insights into classic theories and their relevance to modern machine learning paradigms.

• YouTube
• 1 hour 17 minutes
• On-Demand
• Free Video

Harvard CMSA

Robustness Should Not Be at Odds with Accuracy - A Statistical Learning Theory Perspective

Explore the relationship between adversarial robustness and accuracy in deep learning models, examining how locally adaptive measures can maintain both without compromise.

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

Kolmogorov-Seminar

Vapnik-Chervonenkis Theorem and Statistical Learning Theory

Dive into the fundamental Vapnik-Chervonenkis theorem, exploring how VC-dimension relates to machine learning generalization and error rates in hypothesis spaces.

• YouTube
• 2 hours 35 minutes
• On-Demand
• Free Video

Institute for Pure & Applied Mathematics (IPAM)

Generalization Theory in Machine Learning

Explore generalization theory in machine learning, covering traditional methods, deep learning, and statistical learning theory. Gain insights into overcoming the curse of dimensionality and understanding learning bounds.

• YouTube
• 1 hour 11 minutes
• On-Demand
• Free Video