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#Kernel Methods

Showing: 26 courses
Stanford University

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

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Explore kernel methods and Support Vector Machines in machine learning, focusing on advanced techniques for data classification and pattern recognition in complex datasets.
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1 Lesons
1 hour 55 minutes
On-Demand
Free-Video
Broad Institute

The Challenges of Training Infinitely Large Neural Networks

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Explore challenges in training infinitely large neural networks with Mikhail Belkin. Gain insights into foundational advances in machine learning for biomedical applications.
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1 Lesons
1 hour 6 minutes
On-Demand
Free-Video
Institute for Pure & Applied Mathematics (IPAM)

Infinite Limits and Scaling Laws of Neural Networks

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Explore infinite parameter limits of deep neural networks, scaling laws, and their impact on performance. Gain insights into representation learning and computational resource optimization for neural network training.
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1 Lesons
1 hour
On-Demand
Free-Video
Institute for Pure & Applied Mathematics (IPAM)

Comparison and Transfer Between Tasks in Overparameterized Learning

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Explore similarities and differences in classification and regression tasks for overparameterized linear models. Gain insights into task transfer and generalization in high-dimensional regimes.
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1 Lesons
49 minutes
On-Demand
Free-Video
Simons Institute

Neural Networks Meet Nonparametric Regression: Generalization by Weight Decay and Large Learning Rates

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Explore the intersection of deep neural networks and nonparametric regression, uncovering insights on overparameterization, representation learning, and adaptive generalization in machine learning.
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1 Lesons
1 hour 4 minutes
On-Demand
Free-Video
The Julia Programming Language

Interplay of Linear Algebra, Machine Learning, and HPC - JuliaCon 2021 Keynote

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Explore the interplay of linear algebra, machine learning, and HPC with Dr. Li. Learn about fast solvers, low-rank compression, and how ML can optimize linear algebra computations for big data problems.
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34 Lesons
40 minutes
On-Demand
Free-Video
Institute for Advanced Study

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

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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.
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24 Lesons
31 minutes
On-Demand
Free-Video
Institute for Advanced Study

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

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Explore neural models' impact on speech and language processing, examining successes, challenges, and key architectures in parsing, word embeddings, and transformers.
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14 Lesons
45 minutes
On-Demand
Free-Video
Institute for Pure & Applied Mathematics (IPAM)

Tensor Networks for Machine Learning and Applications

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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.
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21 Lesons
31 minutes
On-Demand
Free-Video
Applied Algebraic Topology Network

Alexander Wagner - Nonembeddability of Persistence Diagrams into Hilbert Spaces

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Explore nonembeddability of persistence diagrams in Hilbert spaces, examining stability, metrics, and implications for machine learning applications in topological data analysis.
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10 Lesons
27 minutes
On-Demand
Free-Video
Applied Algebraic Topology Network

On the Metric Distortion of Embedding Persistence Diagrams into RKHS

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Explores embedding Persistence Diagrams into Reproducing Kernel Hilbert Spaces, analyzing metric properties and stability guarantees for machine learning applications in Topological Data Analysis.
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1 Lesons
55 minutes
On-Demand
Free-Video
Applied Algebraic Topology Network

Extracting Persistence Features with Hierarchical Stabilisation

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Explore hierarchical stabilisation for extracting stable invariants from persistence modules, focusing on the stable rank and its applications in data analysis and classification tasks.
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1 Lesons
43 minutes
On-Demand
Free-Video
MITCBMM

Prof. Lorenzo Rosasco- Multiple Kernel Learning

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Explore multiple kernel learning techniques with Prof. Lorenzo Rosasco, covering overlapping groups, vector notation, proxy operators, and more in this comprehensive lecture.
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10 Lesons
1 hour 25 minutes
On-Demand
Free-Video
Alan Turing Institute

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

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Explore the mathematical foundations of inverse problems and machine learning, focusing on representer theorems, Banach spaces, and kernel methods for advanced data science applications.
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22 Lesons
48 minutes
On-Demand
Free-Video
Institute for Pure & Applied Mathematics (IPAM)

Machine Learning Basics: A Speedrun - IPAM at UCLA

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Speedrun through machine learning fundamentals, covering key concepts like inductive bias, regularization, and kernel methods for both linear and nonlinear regression.
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23 Lesons
1 hour 8 minutes
On-Demand
Free-Video
Institute for Pure & Applied Mathematics (IPAM)

Generalization Theory in Machine Learning

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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.
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24 Lesons
1 hour 11 minutes
On-Demand
Free-Video
Institute for Pure & Applied Mathematics (IPAM)

From A to B via a Synthesis of Rare-Event Sampling and Machine Learning

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Explore machine learning and rare-event sampling techniques for analyzing molecular transitions and generating pathways between energy basins, with applications in materials science and biomolecules.
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1 Lesons
45 minutes
On-Demand
Free-Video
MITCBMM

Interpolation and Learning With Scale Dependent Kernels

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Explore scale-dependent kernels in machine learning, covering overfitting, statistical theory, and practical applications in regression and interpolation.
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11 Lesons
1 hour 4 minutes
On-Demand
Free-Video
MITCBMM

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

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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.
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31 Lesons
55 minutes
On-Demand
Free-Video
DataLearning@ICL

Towards Better Understanding of Contrastive Learning

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Explore contrastive learning's foundations, dynamics, and applications in deep models. Gain insights into self-supervised learning, nonlinear analysis, and feature emergence through expert presentation.
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25 Lesons
58 minutes
On-Demand
Free-Video
Conference GSI

Information Theory with Kernel Methods

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Explore entropy estimation in data science using kernel methods and quantum information theory for covariance matrices.
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1 Lesons
59 minutes
On-Demand
Free-Video
Google TechTalks

Fast Neural Kernel Embeddings for General Activations

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Explore fast sketching methods for approximating multi-layered NNGP and NTK matrices with various activation functions, overcoming limitations of prior works and achieving significant speedups.
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1 Lesons
34 minutes
On-Demand
Free-Video
Institut des Hautes Etudes Scientifiques (IHES)

The Quest for Adaptivity in Machine Learning - Comparing Popular Methods

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Explore supervised learning algorithms' strengths and weaknesses, comparing k-nearest-neighbor, kernel methods, and neural networks for adaptivity and performance across various problems.
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17 Lesons
32 minutes
On-Demand
Free-Video
HUJI Machine Learning Club

Gauss-Legendre Features for Scalable Gaussian Process Regression

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Discover how Gauss-Legendre features enable efficient scaling of Gaussian process regression through low-rank kernel matrix approximation, offering faster computation for large datasets with poly-logarithmic feature requirements.
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1 Lesons
1 hour 5 minutes
On-Demand
Free-Video
Probabilistic AI School

Gaussian Processes

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Explore Gaussian Processes with Daniel Hernández-Lobato, covering key concepts, applications, and advanced techniques in probabilistic machine learning.
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1 Lesons
2 hours 22 minutes
On-Demand
Free-Video
Inside Livermore Lab

A First-Principles Approach to Understanding Deep Learning

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Delve into the fundamental principles of deep learning through statistical physics, exploring connections between neural networks, Gaussian processes, and scaling laws with insights from a DeepMind researcher.
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1 Lesons
1 hour 18 minutes
On-Demand
Free-Video