Statistical Learning Theory Courses and Certifications

Machine Learning for Engineers: Algorithms and Applications

Master practical machine learning algorithms through hands-on implementation in Python and PyTorch, covering supervised and unsupervised learning techniques for real-world applications in computer vision, NLP, and robotics.

• Coursera
• 17 hours 31 minutes
• Self-Paced
• Paid Course

Statistical Learning for Engineering Part 1

Explore supervised and unsupervised machine learning algorithms, from linear regression to SVMs, with hands-on Python implementation for real-world engineering applications.

• Coursera
• 1 day 11 hours 38 minutes
• Self-Paced
• Paid Course

Foundations of Statistical Learning & Algorithms

Master fundamental mathematical concepts for machine learning through linear algebra, probability theory, and optimization techniques, focusing on practical applications and model evaluation methods.

• Coursera
• 21 hours 22 minutes
• Self-Paced
• Paid Course

Statistical Learning for Engineering Part 2

Explore advanced machine learning algorithms including neural networks, clustering, dimensionality reduction, and reinforcement learning with hands-on Python implementation.

• Coursera
• 1 day 46 minutes
• Self-Paced
• Paid Course

Packt

Fundamentals of Machine Learning

Discover machine learning fundamentals through hands-on labs covering regression, classification, decision trees, SVM, and deep learning with real-world applications.

• Coursera
• 12 hours 16 minutes
• Self-Paced
• Paid Course

Supervised machine learning and performance evaluation

Master supervised machine learning evaluation with cross-validation, nested resampling, and model selection techniques to build robust, generalizable predictive models with confidence.

• Coursera
• 4 hours
• Self-Paced
• Paid Course

Centre International de Rencontres Mathématiques

Regularization via Permutation of Labels - Lecture 1

Explore regularization techniques using label permutation in mathematical statistics, focusing on innovative approaches and their applications.

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

Centre International de Rencontres Mathématiques

Regularization via Permutation of Labels - Lecture 2

Explore advanced statistical techniques for regularization through label permutation, focusing on mathematical theory and practical applications.

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

Harvard CMSA

Are Gaussian Data All You Need for Machine Learning Theory - A Statistical Physics Perspective

Delve into the theoretical foundations of Gaussian assumptions in machine learning, exploring their surprising effectiveness and limitations in high-dimensional statistics and modeling.

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

STAT 508: Applied Data Mining & Statistical Learning

Explore statistical learning and data mining methods using R, covering predictive modeling, clustering, and dimension reduction techniques for analyzing complex datasets.

• OPEN.ED@PSU
• Self-Paced
• Free Online Course

The 3rd TBSI Workshop on Learning Theory

Explore cutting-edge developments in machine learning theory, optimization algorithms, and statistical inference through expert-led discussions and research presentations.

• XuetangX
• Self-Paced
• Free Online Course

University of Tübingen

Statistical Machine Learning

2 ratings

Comprehensive exploration of machine learning concepts, from basic algorithms to advanced topics like kernels, dimensionality reduction, and clustering, with insights on societal impact and practical applications.

• YouTube
• 1 day 9 hours
• Self-Paced
• Free Online Course

Computational Principles Underlying the Learning of Sensorimotor Repertoires

Explore computational principles of motor learning, focusing on context's role in memory activation and how statistical learning shapes object representations.

• YouTube
• 1 hour 5 minutes
• Self-Paced
• Free Video

Pascal Poupart

Introduction to Machine Learning - Spring 2019 - University of Waterloo

Dive into comprehensive machine learning fundamentals covering neural networks, SVMs, deep learning, CNNs, transformers, and ensemble methods from University of Waterloo.

• YouTube
• 1 day 4 hours 26 minutes
• Self-Paced
• Free Video

Neural Networks in R and the MNIST Data - Lecture 10.R.1

Explore neural networks using R and MNIST data, learning practical implementation of deep learning techniques for image recognition and statistical analysis.

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