Machine Learning Course
Comprehensive exploration of machine learning fundamentals, covering linear models, neural networks, SVMs, and key principles for effective algorithm implementation and evaluation.
Learning To See
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.
The Information Knot - Tying Sensing and Action; Emergence Theory of Representation Learning
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.
Does Generalization Imply Accuracy on the Line? A New Look at Robust Generalization
Explore the relationship between generalization and accuracy in machine learning, examining robust generalization through a fresh perspective.
Unexpected Test Losses from Generalization Theory
Dive into generalization theory's impact on unexpected test losses, exploring emerging settings and their implications for machine learning models.
Generalization from the Behavioral Perspective
Dive into the behavioral perspective of generalization, exploring emerging settings and their implications in machine learning and data analysis.
Bias Variance Tradeoff
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.
Generalisation of Some Overparametrised Models
Explorez la généralisation des modèles surparamétrés avec Stéphane Chrétien. Plongez dans les concepts mathématiques avancés et leurs applications dans l'apprentissage et l'optimisation.
Statistical Inference I
Explore statistical inference fundamentals, including models, estimation, minimax risk, and Bayesian approaches, with UC Berkeley's Will Fithian.
Beating the Odds - Learning or Hallucinating? The Science of Data Today
Explore the intersection of data science, learning, and hallucination with insights from a renowned mathematician on current trends and challenges in the field.
Reconsidering Overfitting in the Age of Overparameterized Models
Explore modern machine learning paradigms, focusing on overparameterized models' ability to generalize well despite near-zero training error, challenging classical overfitting concepts.
Regression Trees, Clearly Explained
Clear explanation of regression trees, their applications, and step-by-step construction process. Covers differences from classification trees and use with multiple variables.
Supervised Machine Learning Review
Comprehensive review of supervised machine learning concepts, focusing on statistical frameworks, loss functions, and the intriguing double-descent phenomenon in neural networks.
Overfitting: Benign, Tempered and Harmful - Lecture on Machine Learning Regularization
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.
Training Neural Networks and Deep Learning Optimization Methods - Lecture 16
Master neural network training fundamentals, from momentum methods and regularization to batch normalization, with practical applications in BBB transmission prediction and model optimization.
Modern Paradigms of Generalization - The Heliocentric Model of Aristarchus
Explore modern generalization paradigms and historical scientific models in this insightful lecture on machine learning theory and its historical parallels.
From Simplicity to Complexity: The Double Descent Effect in Modern Machine Learning
Explore the double descent effect in modern machine learning, transitioning from simplicity to complexity. Gain insights into this intriguing phenomenon and its implications.