SPSS Tutorial for Data Analysis - SPSS for Beginners
Comprehensive SPSS tutorial covering various statistical analyses, from basic tests to advanced regression techniques. Ideal for beginners seeking practical data analysis skills using IBM SPSS Statistics.
Machine Learning
Comprehensive introduction to machine learning concepts, from fundamentals to advanced techniques, with clear explanations and practical applications.
Deep Generative Models - Lecture 5: Variational Autoencoders
Explore Variational Autoencoders (VAEs) in deep generative modeling, covering theoretical foundations and practical applications in this comprehensive Stanford lecture.
Deep Generative Models - Lecture 6: Variational Autoencoders
Explore Variational Autoencoders (VAEs) in deep generative modeling, covering theoretical foundations and practical applications in this Stanford lecture by Prof. Stefano Ermon.
Blessings of Dimensionality for Gaussian Latent Factor Models
Explore how high-dimensional data analysis can benefit from restricted latent factor overlaps, with applications in brain fMRI and financial markets.
MPG Primer: Understanding Single Cell ATAC-Seq Data Analysis (2024)
Explore single-cell ATAC-seq data analysis, covering key concepts, workflow, quality control, and downstream analysis techniques for epigenomic profiling at cellular resolution.
Temporal Network Embedding Using Classical Multidimensional Scaling
Explore temporal network embedding techniques using classical multidimensional scaling for advanced data analysis and visualization.
Multivariate
Explore advanced multivariate statistical concepts, including hypothesis testing, probability distributions, and data analysis techniques, with practical applications using R programming.
The Fun and Easy Guide to Machine Learning - Tutorials
Comprehensive introduction to machine learning algorithms, from regression to neural networks, with practical Python implementations and engaging whiteboard explanations for beginners and enthusiasts.
Principal Component Analysis - Lecture 8
Explore Principal Component Analysis: from matrix rank to SVD. Learn to measure data cloud size, understand correlated data, and navigate high-dimensional spaces using statistical techniques.
Multidimensional Scaling by Particle Swarm Optimization
Explora el escalamiento multidimensional mediante optimización por enjambre de partículas con el experto Javier Trejos.
Sign-Rank vs Margin
Exploring the relationship between sign-rank and margin in boolean matrices, with applications in communication complexity and machine learning. Discusses key questions and provides negative answers using various mathematical tools.
Principal Component Analysis
Explore dimensionality reduction through PCA, covering variance, covariance, eigenvectors, and eigenvalues. Learn key concepts with visual explanations and practical applications in data analysis.
Sketching for Linear Algebra - Basics of Dimensionality Reduction and CountSketch I
Explore dimensionality reduction and CountSketch techniques for efficient linear algebra computations, focusing on regression analysis and subspace embeddings.
A Topological Study of the Space of Planar Pentagons
Exploring topology of planar pentagons as a model for molecular conformation spaces, using topological data analysis for dimensionality reduction and visualization.
Practical Clustering and Topological Data Analysis
Explore clustering algorithms and topological data analysis from a data science perspective, covering techniques from k-means to DBSCAN and dimensionality reduction methods like UMAP.
Sketching for Linear Algebra - Basics of Dimensionality Reduction and CountSketch II
Explore dimensionality reduction and CountSketch techniques in linear algebra, focusing on advanced applications and theoretical foundations for data science.
Visualizing and Analyzing Event Sequence Data
Explore techniques for analyzing event sequence data, including causal inference, deep learning, and generative AI, to extract valuable insights from high-dimensional business process data.
Functional Data Analysis in Practice
Explore practical applications of functional data analysis with insights from Qiwei Yao of the London School of Economics, covering key concepts and methodologies.
Singular Value Decomposition and Image Compression
Learn singular value decomposition (SVD) and its application in image compression. Explore matrix transformations, dimensionality reduction, and practical implementation techniques.
A Friendly Introduction to Linear Algebra for ML
Explore key linear algebra concepts for machine learning, including data representations, vector embeddings, and dimensionality reduction, with insights from a postdoc at X.