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Data Representations and Processing
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Related lectures (30)
Introduction to Machine Learning: Course Overview and Basics
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Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.
Clustering & Density Estimation
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Covers clustering, PCA, LDA, K-means, GMM, KDE, and Mean Shift algorithms for density estimation and clustering.
Neural Networks Recap: Activation Functions
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Covers the basics of neural networks, activation functions, training, image processing, CNNs, regularization, and dimensionality reduction methods.
Boltzmann Machine
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Covers the Boltzmann Machine, a type of stochastic recurrent neural network.
Dimensionality Reduction: PCA & t-SNE
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Explores PCA and t-SNE for reducing dimensions and visualizing high-dimensional data effectively.
Total scattering and PDF analysis
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Explores total scattering and PDF analysis in materials science, covering in-situ synthesis, data analysis techniques, and applications in host-guest systems.
Data Representations & Processing
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Explores data representations, overfitting, model selection, cross-validation, and imbalanced data challenges.
Feature Engineering: Polynomial Regression
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Covers fitting linear regression on features of the original predictors for flexible feature representation.
Spectral Estimation Methods
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Explores parametric spectrum estimation methods, including line and smooth spectra, and delves into heart rate variability analysis.
Machine Learning Basics
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Introduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.
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