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#Bias-Variance Tradeoff

Showing: 17 courses
California Institute of Technology

Machine Learning Course

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Comprehensive exploration of machine learning fundamentals, covering linear models, neural networks, SVMs, and key principles for effective algorithm implementation and evaluation.
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18 Lesons
1 day
On-Demand
Free-Video

Learning To See

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In this series, we'll explore the complex landscape of machine learning and artificial intelligence through one example from the field of computer vision: using a decision tree to count the number of fingers in an image. It's gonna be crazy.
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15 Lesons
2 hours 30 minutes
On-Demand
Free-Video
New York University (NYU)

The Information Knot - Tying Sensing and Action; Emergence Theory of Representation Learning

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Explore information theory, representation learning, and AI concepts with NYU's Stefano Soatto. Delve into information bottlenecks, mutual information, and disentangling in this advanced seminar on modern AI.
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27 Lesons
55 minutes
On-Demand
Free-Video
Simons Institute

Does Generalization Imply Accuracy on the Line? A New Look at Robust Generalization

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Explore the relationship between generalization and accuracy in machine learning, examining robust generalization through a fresh perspective.
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1 Lesons
1 hour 7 minutes
On-Demand
Free-Video
Simons Institute

Unexpected Test Losses from Generalization Theory

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Dive into generalization theory's impact on unexpected test losses, exploring emerging settings and their implications for machine learning models.
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1 Lesons
1 hour 2 minutes
On-Demand
Free-Video
Simons Institute

Generalization from the Behavioral Perspective

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Dive into the behavioral perspective of generalization, exploring emerging settings and their implications in machine learning and data analysis.
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1 Lesons
1 hour 3 minutes
On-Demand
Free-Video
Great Learning

Bias Variance Tradeoff

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Explore the bias-variance tradeoff in machine learning, understanding its impact on model performance and learning strategies to balance these crucial factors for optimal predictions.
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10 Lesons
29 minutes
On-Demand
Free-Video
Centre International de Rencontres Mathématiques

Generalisation of Some Overparametrised Models

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Explorez la généralisation des modèles surparamétrés avec Stéphane Chrétien. Plongez dans les concepts mathématiques avancés et leurs applications dans l'apprentissage et l'optimisation.
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1 Lesons
35 minutes
On-Demand
Free-Video
Simons Institute

Statistical Inference I

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Explore statistical inference fundamentals, including models, estimation, minimax risk, and Bayesian approaches, with UC Berkeley's Will Fithian.
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8 Lesons
56 minutes
On-Demand
Free-Video
Institute for Mathematical Sciences

Beating the Odds - Learning or Hallucinating? The Science of Data Today

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Explore the intersection of data science, learning, and hallucination with insights from a renowned mathematician on current trends and challenges in the field.
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1 Lesons
1 hour 17 minutes
On-Demand
Free-Video
Simons Institute

Reconsidering Overfitting in the Age of Overparameterized Models

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Explore modern machine learning paradigms, focusing on overparameterized models' ability to generalize well despite near-zero training error, challenging classical overfitting concepts.
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1 Lesons
1 hour 18 minutes
On-Demand
Free-Video
StatQuest with Josh Starmer

Regression Trees, Clearly Explained

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Clear explanation of regression trees, their applications, and step-by-step construction process. Covers differences from classification trees and use with multiple variables.
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6 Lesons
23 minutes
On-Demand
Free-Video
Paul Hand

Supervised Machine Learning Review

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Comprehensive review of supervised machine learning concepts, focusing on statistical frameworks, loss functions, and the intriguing double-descent phenomenon in neural networks.
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11 Lesons
47 minutes
On-Demand
Free-Video
Institute for Pure & Applied Mathematics (IPAM)

Overfitting: Benign, Tempered and Harmful - Lecture on Machine Learning Regularization

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Explore the nuanced understanding of overfitting in neural networks, examining benign, tempered, and harmful effects on model performance based on data properties and training conditions.
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1 Lesons
51 minutes
On-Demand
Free-Video
Manolis Kellis

Training Neural Networks and Deep Learning Optimization Methods - Lecture 16

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Master neural network training fundamentals, from momentum methods and regularization to batch normalization, with practical applications in BBB transmission prediction and model optimization.
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9 Lesons
1 hour 17 minutes
On-Demand
Free-Video
Simons Institute

Modern Paradigms of Generalization - The Heliocentric Model of Aristarchus

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Explore modern generalization paradigms and historical scientific models in this insightful lecture on machine learning theory and its historical parallels.
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1 Lesons
1 hour 10 minutes
On-Demand
Free-Video
Banach Center

From Simplicity to Complexity: The Double Descent Effect in Modern Machine Learning

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Explore the double descent effect in modern machine learning, transitioning from simplicity to complexity. Gain insights into this intriguing phenomenon and its implications.
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1 Lesons
1 hour 24 minutes
On-Demand
Free-Video