Statistical Learning Theory Courses and Certifications

Statistical Learning

Learn statistical learning in Python: linear regression with ridge and LASSO, LDA and logistic classification, splines, kernel smoothing, model selection, maximum likelihood, SVMs and neural networks.

• Coursera
• 9 weeks, 13 hours a week
• Self-Paced
• Paid Course

Statistical Machine Learning

Statistical Machine Learning is a second graduate level course in advanced machine learning, assuming students have taken Machine Learning and Intermediate Statistics. The course covers methodology and theoretical foundations.

• Independent
• Self-Paced
• Free Online Course

The 3rd TBSI Workshop on Learning Theory

A workshop focused on learning theory.

• XuetangX
• Self-Paced
• Free Online Course

Data Science Dojo

VSM, LSA, & SVD - Introduction to Text Analytics with R

Explore advanced text analytics techniques: VSM, LSA, and SVD. Learn to handle high-dimensional data, uncover latent semantic structures, and efficiently map new data in reduced dimensions.

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

機器學習基石上 (Machine Learning Foundations)---Mathematical Foundations

939 ratings at Coursera

以 PAC 學習、VC 維度與泛化理論建立機器學習的數學基礎，分析訓練、測試及雜訊下的學習。

• Coursera
• 8 weeks, 1 hour a week
• Self-Paced
• Paid Course

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
• Self-Paced
• 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
• Self-Paced
• 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
• Self-Paced
• Free Video

UofU Data Science

Understanding Overfitting in Machine Learning with Decision Trees - Lecture 5a

Understand how overfitting occurs in machine learning models, with practical examples using decision trees to identify and prevent this common challenge.

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

Learning from Data (Introductory Machine Learning course)

10 ratings

Learn the theory and practice of machine learning: VC dimension and generalization, bias-variance tradeoff, linear models, neural networks, regularization, validation, support vector machines and kernel methods.

• Independent
• 10 weeks
• Self-Paced
• Free Online Course

机器学习数学基础

系统讲解机器学习理论分析的数学基础，涵盖可学性、复杂度、泛化界、稳定性、一致性、收敛率与在线学习遗憾界。

• XuetangX
• Self-Paced
• Free Online Course

AI TIME：随机梯度方法的学习理论

AI TIME：随机梯度方法的学习理论

• XuetangX
• Self-Paced
• Free Online Course

Statistical Learning with Python

Master statistical learning fundamentals through hands-on Python implementation, covering regression, classification, neural networks, and unsupervised methods without heavy mathematics.

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

Statistical Learning with R

Master supervised and unsupervised learning techniques using R, from linear regression to deep learning, with hands-on implementation of modern data science methods.

• YouTube
• 20 hours 28 minutes
• Self-Paced
• Free Video

Performance measures and validation methods

Evaluate classification models with ROC analysis, AUC and C-index, and apply leave-subject-out cross-validation to repeated-measurement biosignal data for unbiased performance estimates.

• Coursera
• 3 weeks, 1 hour a week
• Self-Paced
• Paid Course