Dictionary of Applied Machine Learning MCP Resources
Dictionary of Applied Machine Learning offers 120 resources. A resource is data the server exposes for a client to attach as context — a file, a page, an API response — rather than an action. Each has its own page, per client.
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Reading order: Wie funktioniert ChatGPT? (LLMs) -
Reading order: KI-Grundlagen -
Reading order: Erklärbare & vertrauenswürdige KI -
Reading order: KI, Recht & AI Act -
Reading order: KI in der Praxis (ML-Systeme) -
Reading order: Mathematik der KI -
accuracy -
artificial neural network (ANN) -
attention -
autoencoder -
backpropagation -
bagging -
base learner -
baseline -
binary classification -
boosting -
bootstrap -
concept activation vector (CAV) -
classification -
clustering -
confusion matrix -
convex -
correlation -
covariance matrix -
data -
data augmentation -
data imputation -
data leakage -
data point -
dataset -
decision boundary -
decision tree -
dimensionality reduction -
distribution shift -
effective dimension -
eigenvalue -
expectation–maximization (EM) -
ensemble -
empirical risk minimization (ERM) -
eigenvalue decomposition (EVD) -
explainability -
explanation -
fairness (machine learning) -
feature learning -
feature -
feature importance -
feature matrix -
feature vector -
fine-tuning -
Gaussian random variable (Gaussian RV) -
gradient descent (GD) -
generalization -
Gaussian mixture model (GMM) -
gradient -
Hilbert space -
hyperparameter -
hypothesis space -
hypothesis -
independent and identically distributed (i.i.d.) -
independent and identically distributed assumption (i.i.d. assumption) -
inner product -
interpretability -
interpretable machine learning (interpretable ML) -
inverse -
kernel method -
kernel ridge regression -
k-fold cross-validation (k-fold CV) -
k-means -
label -
learning rate -
linear classifier -
linear regression -
large language model (LLM) -
logistic loss -
logistic regression -
loss -
matrix -
mean -
membership inference attack -
machine learning pipeline (ML pipeline) -
model -
model selection -
norm -
online learning -
overfitting -
principal component analysis (PCA) -
policy (reinforcement learning) -
pretraining -
probability distribution -
projection -
random experiment -
random forest -
regularization -
reinforcement learning (RL) -
reward -
robust principal component analysis (robust PCA) -
random variable (RV) -
sample size -
self-supervised learning -
sigmoid function -
Sobolev space -
stochastic gradient descent (SGD) -
supervised learning -
singular value decomposition (SVD) -
support vector machine (SVM) -
tabular data -
trusted execution environment (TEE) -
test set -
training error -
training -
training set -
transfer learning -
transformer -
transparency -
trustworthy artificial intelligence (trustworthy AI) -
unsupervised learning -
validation error -
validation set -
variance -
explainable artificial intelligence (XAI)
Read from the server itself, by connecting to it and calling resources/list on 5 November 2026.
A listing does not declare its resources, so asking is the only way to know them, and this
is what the server actually offers rather than what its listing claims. Names only are
stored; connect the server for each resource's type and contents.