Statistical Learning Theory Courses and Certifications

MITCBMM

Generalization Error and Stability

Explore statistical learning theory, focusing on generalization error, stability, and empirical risk minimization. Gain insights into universal consistency and the law of large numbers.

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

MITCBMM

Regularized Least Squares

Explore statistical learning theory with Lorenzo Rosasco, focusing on regularized least squares, optimization techniques, and key concepts in machine learning algorithms.

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

Institute for Advanced Study

Convergence of Nearest Neighbor Classification - Sanjoy Dasgupta

Explore nearest neighbor classification's convergence, consistency, and accuracy rates in various spaces, with insights on smoothness conditions and adaptive classifiers.

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

Data Science Courses

PAC Learnability in Deep Learning - Lecture 20

Delve into statistical machine learning fundamentals, exploring PAC learnability concepts and their application in deep learning, with focus on generalization theories and practical challenges.

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

International Centre for Theoretical Sciences

Almost Optimal Variance-Constrained Best Arm Identification - Lecture 2

Explores advanced techniques in variance-constrained best arm identification, focusing on optimal algorithms and theoretical analysis for efficient decision-making in multi-armed bandit problems.

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

Hausdorff Center for Mathematics

On the Variance and Admissibility of Empirical Risk Minimization on Convex Classes

Explores empirical risk minimization's variance and admissibility in function estimation, proving its minimax optimal variance and admissibility using concentration of measure techniques in convex geometry.

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

IPhT-TV

Mathematical Problems in Machine Learning - Lecture 4

Delve into advanced mathematical probability concepts applied to machine learning through Stanford expert Andrea Montanari's comprehensive theoretical exploration and analysis.

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

IPhT-TV

Mathematical Problems in Machine Learning - Part 1 of 4

Dive into mathematical probability fundamentals for machine learning through Stanford expert Andrea Montanari's comprehensive exploration of key theoretical concepts and applications.

• YouTube
• 1 hour 45 minutes
• On-Demand
• Free Video

Georgia Tech Research

Compatibility and the Lasso - Lecture 2

Explore compatibility and the Lasso in statistical learning with Sara van de Geer from ETH Zurich in this distinguished lecture on advanced machine learning techniques.

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

International Centre for Theoretical Sciences

Asymptotic Optimality of Confidence Interval Based Algorithms for Fixed Confidence

Explore asymptotic optimality of confidence interval algorithms for fixed confidence settings in data science and machine learning applications.

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

Harvard CMSA

Implicit Regularization via Uniform Convergence in Linear Models and Kernel Methods

Explore the statistical analysis of mirror descent algorithms and their convergence properties in machine learning, focusing on early-stopped methods and uniform learning theory applications.

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

UofU Data Science

The VC Dimension in Machine Learning - Lecture 18

Delve into the fundamental concept of VC dimension in machine learning theory, exploring its relationship with shattering and its implications for learning algorithms.

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

UofU Data Science

Machine Learning: Understanding Hypothesis Spaces Through Shattering - Lecture 17

Delve into the concept of shattering and its application in analyzing infinite hypothesis spaces within machine learning theory and computational learning frameworks.

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

HUJI Machine Learning Club

Mean Estimation in High-Dimensional Binary Markov Gaussian Mixture Models

Delve into high-dimensional mean estimation in binary Markov Gaussian mixture models, exploring how memory in data affects estimation error and the interplay between sample size, dimension, and signal strength.

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

HUJI Machine Learning Club

Local Glivenko-Cantelli: Estimating the Mean in Infinite Dimensions

Delve into advanced statistical theory exploring mean estimation in infinite dimensions, focusing on Local Glivenko-Cantelli bounds and maximal deviation analysis for Boolean cube distributions.

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