Kernel Methods Courses and Certifications

MITCBMM

Fit Without Fear - An Over-Fitting Perspective on Modern Deep and Shallow Learning

Explore over-fitting in modern machine learning, challenging conventional wisdom and presenting kernel methods as a competitive alternative to deep learning. Insights on interpolation, generalization, and stochastic gradient descent.

• YouTube
• 55 minutes
• On-Demand
• Free Online Course

Alan Turing Institute

The Mother of All Representer Theorems for Inverse Problems and Machine Learning - Michael Unser

Explore the mathematical foundations of inverse problems and machine learning, focusing on representer theorems, Banach spaces, and kernel methods for advanced data science applications.

• YouTube
• 48 minutes
• On-Demand
• Free Online Course

Stanford CS229 - Machine Learning - Summer 2019 - Kernel Methods & Support Vector Machine

Explore kernel methods and Support Vector Machines in machine learning, focusing on advanced techniques for data classification and pattern recognition in complex datasets.

• YouTube
• 1 hour 55 minutes
• On-Demand
• Free Online Course

Illinois Institute of Technology

Statistical Learning

Explore cutting-edge statistical analysis techniques, from regression to advanced methods like kernel smoothing and support vector machines. Enhance data interpretation skills with hands-on Python examples.

• Coursera
• 4 days 19 hours 5 minutes
• On-Demand
• Free Online Course (Audit)

University of Tübingen

Statistical Machine Learning

1 review

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
• On-Demand
• Free Online Course

Institute for Advanced Study

On the Connection Between Neural Networks and Kernels: A Modern Perspective - Simon Du

Explore connections between neural networks and kernels, focusing on over-parameterization, generalization, and neural tangent kernels. Insights on training dynamics and applications to various learning tasks.

• YouTube
• 31 minutes
• On-Demand
• Free Online Course

Institute for Advanced Study

Successes and Challenges in Neural Models for Speech and Language - Michael Collins

Explore neural models' impact on speech and language processing, examining successes, challenges, and key architectures in parsing, word embeddings, and transformers.

• YouTube
• 45 minutes
• On-Demand
• Free Online Course

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 Online Course

MITCBMM

Prof. Lorenzo Rosasco- Multiple Kernel Learning

Explore multiple kernel learning techniques with Prof. Lorenzo Rosasco, covering overlapping groups, vector notation, proxy operators, and more in this comprehensive lecture.

• YouTube
• 1 hour 25 minutes
• On-Demand
• Free Online Course

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 Online Course

Institute for Pure & Applied Mathematics (IPAM)

Machine Learning Basics: A Speedrun - IPAM at UCLA

Speedrun through machine learning fundamentals, covering key concepts like inductive bias, regularization, and kernel methods for both linear and nonlinear regression.

• YouTube
• 1 hour 8 minutes
• On-Demand
• Free Online Course

Institute for Pure & Applied Mathematics (IPAM)

Tensor Networks for Machine Learning and Applications

Explore tensor networks in machine learning, their benefits, and applications. Learn about model functions, optimization algorithms, and potential insights for matching architectures to data types.

• YouTube
• 31 minutes
• On-Demand
• Free Online Course

Applied Algebraic Topology Network

Alexander Wagner - Nonembeddability of Persistence Diagrams into Hilbert Spaces

Explore nonembeddability of persistence diagrams in Hilbert spaces, examining stability, metrics, and implications for machine learning applications in topological data analysis.

• YouTube
• 27 minutes
• On-Demand
• Free Online Course

Applied Algebraic Topology Network

On the Metric Distortion of Embedding Persistence Diagrams into RKHS

Explores embedding Persistence Diagrams into Reproducing Kernel Hilbert Spaces, analyzing metric properties and stability guarantees for machine learning applications in Topological Data Analysis.

• YouTube
• 55 minutes
• On-Demand
• Free Online Course

Applied Algebraic Topology Network

Extracting Persistence Features with Hierarchical Stabilisation

Explore hierarchical stabilisation for extracting stable invariants from persistence modules, focusing on the stable rank and its applications in data analysis and classification tasks.

• YouTube
• 43 minutes
• On-Demand
• Free Online Course