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Related lectures (54)
Dimensionality Reduction: PCA and LDA
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Covers dimensionality reduction techniques like PCA and LDA, clustering methods, density estimation, and data representation.
Unsupervised Learning: PCA & K-means
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Covers unsupervised learning with PCA and K-means for dimensionality reduction and data clustering.
K-means Algorithm
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Covers the K-means algorithm for clustering data samples into k classes without labels, aiming to minimize the loss function.
Clustering & Density Estimation
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Covers dimensionality reduction, clustering, and density estimation techniques, including PCA, K-means, GMM, and Mean Shift.
Clustering & Density Estimation
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Covers dimensionality reduction, PCA, clustering techniques, and density estimation methods.
Document Analysis: Topic Modeling
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Explores document analysis, topic modeling, and generative models for data generation in machine learning.
Clustering Methods
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Covers K-means, hierarchical, and DBSCAN clustering methods with practical examples.
Machine Learning Fundamentals
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Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.
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.
Unsupervised Learning: Clustering and Dimension Reduction
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Covers unsupervised learning, clustering, and dimension reduction techniques.
Introduction to Image Classification
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Covers image classification, clustering, and machine learning techniques like dimensionality reduction and reinforcement learning.
Boltzmann Machine
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Covers the Boltzmann Machine, a type of stochastic recurrent neural network.
Clustering: Unsupervised Learning
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Covers clustering algorithms, evaluation methods, and practical applications in machine learning.
Predicting Rainfall: Miniproject BIO-322
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Introduces a miniproject where students predict rainfall in Pully using machine learning, focusing on reproducibility and code quality.
Data Mining: Introduction
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Covers the challenges and opportunities of data mining, practical questions, algorithm components, and applications like shopping basket analysis.
Topic Models: Latent Dirichlet Allocation
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Covers topic models, focusing on Latent Dirichlet Allocation, clustering, GMMs, Dirichlet distribution, LDA learning, and applications in digital humanities.
Untitled
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Machine Learning Fundamentals: Structure Discovery, Classification, Regression
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Covers fundamental machine learning concepts including Structure Discovery, Classification, and Regression.
Statistical Signal Processing
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Covers Gaussian Mixture Models, Denoising, Data Classification, and Spike Sorting using Principal Component Analysis.
Evaluating Clustering Quality
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Explores the silhouette coefficient for evaluating cluster quality and other cluster evaluation measures.
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