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Data Mining and Machine Learning Jupyter Book
Welcome to the Data Mining and Machine Learning Book
Notation
Linear Algebra
Vector Spaces
Normed Vector Spaces
Exercises
Optimization
Optimization Problems
Convex Optimization
Analytic Solutions
Numerical Optimization
Matrix Derivatives
Exercises
Regression
Regression Objective
Regression Functions
Minimizing the RSS
The Bias-Variance Tradeoff
The Sparse Regression Task
Ridge Regression
Lasso
Exercises
Classification
Classification Objective
K-Nearest Neighbor
Naive Bayes
Decision Trees
Random Forests
Support Vector Machines
Kernel SVM
Evaluation
Exercises
Neural Networks
From Linear Models to Neural Networks
MLPs
Backpropagation
SGD
Convolution
Architecture
Dimensionality Reduction Techniques
Low Rank Matrix Factorization
Matrix Completion
Principal Component Analysis
Exercises
Clustering
k-Means
k-Means is MF
Kernel k-means
Spectral Clustering
Exercises
Bibliography
repository
open issue
suggest edit
.ipynb
.pdf
Matrix Completion
Matrix Completion
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