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Learning control laws with DS
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Related lectures (35)
Kernel Regression: K-nearest Neighbors
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Covers the concept of kernel regression and K-nearest neighbors for making data linearly separable.
Regression: High Dimensions
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Explores linear regression in high dimensions and practical house price prediction from a dataset.
Machine Learning: Supervised and Unsupervised Learning Techniques
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Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.
Regression: Exercises
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Covers exercises on regression functions using RLS, WLS, and LWR.
Regression: Interactive Lecture
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Covers linear regression, weighted regression, locally weighted regression, support vector regression, noise handling, and eye mapping using SVR.
Linear Regression and Logistic Regression
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Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.
Machine Learning Review
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Covers a review of machine learning concepts, including supervised learning, classification vs regression, linear models, kernel functions, support vector machines, dimensionality reduction, deep generative models, and cross-validation.
Binary Classification by Regression: Decision Functions and Cost Functions
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Explores binary classification by regression, decision functions, and various cost functions.
Machine Learning Applications: Regression and Classification
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Explores machine learning applications in materials modeling, covering regression, classification, and feature selection.
Gradient Descent: Linear Regression
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Covers the concept of gradient descent for linear regression, explaining the iterative process of updating parameters.
Data-Driven Modeling: Regression
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Introduces data-driven modeling with a focus on regression, covering linear regression, risks of inductive reasoning, PCA, and ridge regression.
House Price Regressions: Exploring OLS Assumptions
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Explores OLS regressions for house prices, covering outliers, influential observations, model specification, and selection strategies.
Linear Regression: Causality and Model Interpretation
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Explores linear regression, causality, and model interpretation through geometric analysis and empirical modeling.
Neural Networks: Training and Optimization
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Explores neural network training, optimization, and environmental considerations, with insights into PCA and K-means clustering.
Statistical Learning: Fundamentals
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Introduces the fundamentals of statistical learning, covering supervised learning, decision theory, risk minimization, and overfitting.
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