User:Speediedan/Books/ml2
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- Anomaly detection
- Machine learning
- Unsupervised learning
- Cluster analysis
- Supervised learning
- Statistical classification
- Regression analysis
- Dimensionality reduction
- Structured prediction
- Artificial neural network
- Reinforcement learning
- Feature learning
- Semi-supervised learning
- Meta learning (computer science)
- Inductive bias
- Density estimation
- Generalized linear model
- Random forest
- Time complexity
- Decision tree
- Association rule learning
- Joint probability distribution
- Deep learning
- Information gain in decision trees
- Support vector machine
- Inference
- Bayesian network
- Principal component analysis
- Neural coding
- Multilinear subspace learning
- Recommender system
- Similarity learning
- Genetic algorithm
- Hidden Markov model
- Nearest neighbor search
- Similarity search
- Euclidean distance
- Taxicab geometry
- Metric (mathematics)
- Curse of dimensionality
- K-nearest neighbors algorithm
- Statistical distance
- Singular value decomposition
- Linear least squares (mathematics)
- Locality-sensitive hashing
- Kullback–Leibler divergence
- Information theory
- Probability distribution
- Fisher information metric
- Gibbs' inequality
- Entropy (information theory)
- Self-information
- Bayesian statistics
- Prior probability
- Bayes' theorem
- Riemann hypothesis
- Quantum information science
- Density matrix
- Information gain ratio
- Information theory and measure theory
- Inner product space
- Trigonometric functions
- Information retrieval
- Computer cluster
- Data mining
- Dot product
- Magnitude (mathematics)
- Pearson product-moment correlation coefficient
- Jaccard index
- Bit array
- Hamming distance
- Correlation and dependence
- Euclidean space
- Euclidean vector
- Unit vector
- Matrix multiplication
- Cross product
- Expectation–maximization algorithm
- Maximum likelihood
- Maximum a posteriori estimation
- Latent variable
- Likelihood function
- Statistical model
- Derivative
- Saddle point
- K-means clustering
- Maxima and minima
- Kernel method
- Polynomial expansion
- Factorization of polynomials
- Discrete cosine transform
- Karhunen–Loève theorem
- One-hot
- Norm (mathematics)
- Conjugate transpose
- Transpose
- Minor (linear algebra)
- Square matrix
- Eigenvalues and eigenvectors
- Outer product
- Hadamard product (matrices)
- Chi-squared test
- Pearson's chi-squared test
- Chi-squared distribution
- Null hypothesis
- Central limit theorem
- Test statistic
- Variance
- Fisher's exact test
- Binomial distribution
- P-value
- Time series
- Autocorrelation
- Likelihood-ratio test
- Covariate
- Student's t-distribution
- F-distribution
- Dirichlet distribution
- Conjugate prior
- Kolmogorov–Smirnov test
- Goodness of fit
- Normal distribution
- Cumulative distribution function
- Kernel density estimation
- Nonparametric statistics
- Loss function
- Optimization problem
- Derivative test
- Stochastic control
- Stationary point
- Concave function
- Mean value theorem
- Differentiable function
- Taylor's theorem
- Second derivative
- Second partial derivative test
- Lasso (statistics)
- Gradient descent
- Gauss–Newton algorithm
- Limited-memory BFGS
- Quasi-Newton method
- Hessian matrix
- Receiver operating characteristic
- Similarity measure
- Convex optimization
- Radial basis function kernel
- Cosine similarity
- Hilbert space
- Complete metric space
- Energy distance