Data Science / Machine Learning

(Draft Status)

Probability, Entropy and Statistics


  • Level of measurements
  • Data Handling
  • Data Cleansing

Data Bases

Data Preprocessing, Data Transformations and Dimensionality Reduction

Exploratory Data Analysis and Visualization

Machine Learning

Basics of Machine Learning

Association Rule Learning

Bayesian Statistics, Bayesian Inference

Anomaly Detection

Graphical Models

Directed Graphical Models (Bayesian Networks)

Undirected Graphical Models

Algorithms of Supervised Leaning

Ensemble Learning

  • Bagging
  • Boosting
  • Decision Forests

Neural Networks

Unsupervised Learning


Unsupervised Feature Learning and Manifold Learning

Probability density estimation

  • Neural Autoregressive Density Estimation (NADE)

Reinforcement Learning

Time Series Analysis

Data Integration

Network Analysis


Semantic Data

  • Ontologies
  • RDF - RDFS
  • OWL

Textual data and natural language processing

High Scalability - Data Parallelism

  • Comunication Patterns

Cloud Computing