Notes

AI
Bayesian
Complexity Theory
Constraint Satisfaction Problem
Cryptography
Data Science
- Data
- Data Visualization
- Database
- Dimensionality Reduction
- Machine Learning
  - Classification
  - Clustering
  - Decision Tree
  - Deep Learning
  - Ensemble
  - Evaluation
  - Explainable AI
  - Feature Engineering
  - Natural Language Processing
  - Neural Networks
  - Outlier
  - Random Forest
  - SVM
  - Time Series
  - Attribute Selection Method
  - Automatic Concept-Hierarchy Generation
  - Basis Function Expansion
  - Classification
  - Complexity vs. Generalization Error
  - Custom Loss Function for Linear Regression in Python
  - Decision Boundary
  - Decision List
  - Double Descent
  - Empirical Loss
  - Forest-RC
  - Generalization Loss
  - Grid Search
  - Heuristic Search
  - Kernel Trick
  - Learning Curve
  - Lineare Regression per Hand ausrechnen
  - Loss Function
  - Machine Learning
  - Majority Voting
  - Maximum Margin Separator
  - ML Pipeline
  - Model Deployment
  - Model Selection
  - Model Training
  - Nearest Neighbors Algorithm
  - Nested Cross Validation
  - Norm Loss
  - Normalization
  - Normalization by decimal scaling
  - Numerosity Reduction
  - One-Hot-Encoding
  - Online Learning
  - Optimization of a Linear Model
  - Overfitting
  - Overfitting and Underfitting
  - Performance Monitoring
  - Presidio
  - Quantile Regression
  - Randomized Search
  - Reconstruction Loss
  - Regression
  - Reinforcement Learning
  - Semi-Supervised Learning
  - Sequence To Sequence Model
  - Singular Value Decomposition
  - Sparsity
  - Stratified K-Fold Corss Validation
  - Stratified Sampling
  - Supervised Learning
  - Support Vector Machine
  - Train Validation Split
  - U-shaped Learning
  - Underfitting
  - Unsupervised Learning
- Bias
- Binning
- Contingency table for binary data
- Correlation Analysis
- Cross-Industry Standard Process for Data Mining
- Data Cleaning
- Data Compression
- Data Discretization
- Data Ingestion
- Data Integration
- Data Mining in the ML and Statistics Community
- Data Preparation
- Data Profiling
- Data Quality
- Data Reduction
- Data Segregation
- Data Transformation
- Gaussian Mixture Expectation Maximization
- Gewichteter Mittelwert
- Histogram Analysis
- Log-Likelihood
- Metriken
- Min-Max Normalization
- Sample
- Sampling
- Simple Matching Coefficient
Decision Theory
Distances
Fourier Transform
Frequent Item
Graph
Information Theory
Knowledge Representation
Logic
Markov
Mathematics
media
Optimization
Other
Pattern Recognition
Search
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Statistics
INDEX
MOC - Introduction to Statistics and Statistical Programming
MOC - Stochastische Modellbildung

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Reconstruction Loss

Regularisation

Local graph

Data Science
Machine Learning
Norm Loss

Norm Loss

L=\mid x-\hat{x}\mid

No explicit Regularisation here.