Individual Differences in Learning Manual Control of Dynamic Balancing

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Explores the MODNet methodology for material property predictions, emphasizing feature selection and supervised learning.

Regression Trees and Ensemble Methods in Machine Learning

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Exploration Bias

Explores regularization, learning algorithms, and subgaussian assumptions in machine learning.

Supervised Learning: Classification Algorithms

Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.

Introduction to Machine Learning

Covers the basics of Machine Learning, including recognizing hand-written digits, supervised classification, decision boundaries, and polynomial curve fitting.

Growth rate and uniform convergence

Explores growth rate, uniform convergence, PAC learning, and distribution learning challenges.

Introduction to Machine Learning

Provides an overview of Machine Learning, including historical context, key tasks, and real-world applications.

Gaussian Discriminant Rule: Classification & Boundaries

Explores the Gaussian Discriminant Rule for classification using Gaussian Mixture Models and discusses drawing boundaries and model complexity.

Modeling the Brain: Machine Learning and Neuroscience

Explores the synergy between machine learning and neuroscience, showcasing how deep neural networks can predict neural responses and the challenges faced by AI in robotics.

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