Machine learning - Statistical methods.

Introduction to statistical relational learning by: (Language materials, printed)

Boosted statistical relational learners = from benchmarks to data-driven medicine / by: (Electronic resources)

Introduction to statistical machine learning / by: (Electronic resources)

Minimum divergence methods in statistical machine learning = from an information geometric viewpoint / by: (Electronic resources)

Hyperparameter tuning for machine and deep learning with R = a practical guide / by: (Electronic resources)

Learning in the absence of training data by: (Electronic resources)

Applied statistical learning = with case studies in Stata / by: (Electronic resources)

Bayesian tensor decomposition for signal processing and machine learning = modeling, tuning-free algorithms, and applications / by: (Electronic resources)

Elements of dimensionality reduction and manifold learning by: (Electronic resources)

Computer algorithms.

Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.

Data Analysis and Big Data.

Relational databases.

Computational Physics and Simulations.

Numerical Analysis.

Data mining.

Artificial Intelligence (incl. Robotics)

Statistical Theory and Methods.

Digital and Analog Signal Processing.

Stata.

Health Informatics.

R (Computer program language)

Dimension reduction (Statistics)

Signal processing- Statistical methods.

Neural networks (Computer science)

Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy.

Data Mining and Knowledge Discovery.

Computational Intelligence.

Machine learning- Mathematics.

Probability Theory.

Artificial Intelligence.

Machine Learning.

Statistics in Business, Management, Economics, Finance, Insurance.

Machine learning- Statistical methods.

Computer Science.

Probability and Statistics in Computer Science.

Deep learning (Machine learning)

Bayesian statistical decision theory.

Statistical Software.

Statistical Learning.

Bayesian Inference.