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Related lectures (28)
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Generative Models: Self-Attention and Transformers
Covers generative models with a focus on self-attention and transformers, discussing sampling methods and empirical means.
Regression Trees and Ensemble Methods in Machine Learning
Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.
Machine Learning Biases
Covers the basics of machine learning, challenges in deployment, adversarial attacks, and privacy concerns.
Supervised Learning: Classification Algorithms
Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.
Unsupervised Learning: Clustering
Explores unsupervised learning through clustering techniques, algorithms, applications, and challenges in various fields.
Self-Supervised Image Representation Learning
Presents BYOL, a self-supervised image representation learning method achieving state-of-the-art results without negative pairs.
Supervised Learning: Regression Methods
Explores supervised learning with a focus on regression methods, including model fitting, regularization, model selection, and performance evaluation.
Introduction to Image Classification
Covers image classification, clustering, and machine learning techniques like dimensionality reduction and reinforcement learning.
Supervised Learning in Asset Pricing
Explores supervised learning in asset pricing, focusing on stock return prediction challenges and model assessment.
Model Evaluation
Explores underfitting, overfitting, hyperparameters, bias-variance trade-off, and model evaluation in machine learning.
Overfitting in Supervised Learning: Case Studies and Techniques
Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.
Self-supervised Learning for Autonomous Vehicles
Explores self-supervised learning for autonomous vehicles, deriving labels from data itself and discussing its applications and challenges.
Introduction to Machine Learning
Provides an overview of Machine Learning, including historical context, key tasks, and real-world applications.
Machine Learning: Supervised and Unsupervised Learning Techniques
Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.
Image Representation Insights
Explores the evolution of image representation, challenges in supervised learning, benefits of self-supervised learning, and recent advancements in SSL.
Gradient Descent and Linear Regression
Covers stochastic gradient descent, linear regression, regularization, supervised learning, and the iterative nature of gradient descent.
Transformers in Visual Intelligence
Explores transformers in visual intelligence, focusing on object detection, image synthesis, and feature fusion.
Machine Learning at the Atomic Scale
Explores simple models, electronic structure evaluation, and machine learning at the atomic scale.
Improving Models of the Ventral Visual Pathway
Explores computational models of the ventral visual system, focusing on optimizing networks for real-world tasks and comparing to brain data.
Logistic Regression: Classification
Covers supervised learning, classification using logistic regression, and challenges in optimization.
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