Notes/TU-Machine-Learning

54 items under this folder.

Oct 12, 2025
HW
Mar 22, 2026
Branch-Bound Algorithm
Mar 22, 2026
ADALINE
Mar 22, 2026
Commonly Used Kernels
Mar 22, 2026
Generalized Optimal Hyperplane
Mar 22, 2026
Optimal Hyperplane
Mar 22, 2026
Perceptron
Mar 22, 2026
Usage of SVMs
Mar 22, 2026
Statistical Significance of Regression Coefficient
Dec 03, 2025
TU ML Summary
Nov 04, 2025
t-SNE
Nov 04, 2025
Principal Component Analysis
Nov 04, 2025
Bayesian Estimation
Nov 04, 2025
Maximum Likelihood Estimation
Oct 29, 2025
Neyman-Pearson Criterion
Oct 26, 2025
Bayesian Decision Theory Framework with Costs
Oct 26, 2025
Naive Bayes Classifier
Oct 26, 2025
Markov Chain
Oct 26, 2025
Bayesian Decision with Gaussian Distribution
Oct 26, 2025
Bayesian Rule Forms
Oct 26, 2025
Basic Bayesian Statistics
Oct 26, 2025
Ensemble Learning
Oct 21, 2025
Ada Boost
Oct 21, 2025
Random Forest
Oct 21, 2025
Decision Trees
Oct 21, 2025
Pruning in Decision Trees
Oct 21, 2025
Regression Trees
Oct 21, 2025
Information Gain
Oct 21, 2025
Shannon Entropy
Oct 21, 2025
Feature Extraction
Oct 16, 2025
Karhunen-Loeve Transform
Oct 16, 2025
Separability of Classes
Oct 16, 2025
Statistical Learning Theory
Oct 15, 2025
Kernel Trick
Oct 15, 2025
Mercer Theorem
Oct 15, 2025
Kuhn Tucker theorem
Oct 15, 2025
Linearly Separable
Oct 12, 2025
Linear Regression
Oct 11, 2025
Multi-Layer Perceptron
Oct 11, 2025
Condensed Nearest Neighbor Method
Oct 11, 2025
Editing Nearest Neighbor Method
Oct 11, 2025
K-Nearest Neighbor Method
Oct 11, 2025
Nearest-Neighbor Method
Oct 11, 2025
Distances for NN Methods
Oct 11, 2025
Multi-class Logistic Regression
Oct 11, 2025
Fisher Criterion
Oct 11, 2025
Linear Discriminant Analysis
Oct 11, 2025
Logistic Regression
Oct 11, 2025
Minimum Squared Error
Oct 11, 2025
Classification
Oct 10, 2025
Normalized Augmented Feature Vector
Sep 29, 2025
R2 Goodness of Fit
Sep 29, 2025
Invertible Matrices
Sep 29, 2025
Standard Results of Matrix Calculus

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Notes/TU-Machine-Learning