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

  • Univariate Linear Regression:

  • Maximum Likelihood Principle

  • Multivariate Linear Regression
  • Logistic Regression
  • Overfitting, Underfitting, Bias-Variance Tradeoff
  • Regularization
  • Validation

Association Rule Learning

Bayesian Statistics, Bayesian Inference

Variational Methods

Anomaly Detection

Graphical Models

Directed Graphical Models (Bayesian Networks)

Undirected Graphical Models


Supervised Leaning

Ensemble Learning

  • Bagging
  • Boosting
  • Decision Forests

Neural Networks

Unsupervised Learning

Probability density estimation, Unsupervised Feature Learning and Manifold Learning

  • Neural Autoregressive Density Estimation (NADE)
  • Variational Autoencoder

Reinforcement Learning

Time Series Analysis

  • AR, MA, AR(I)MA
  • Hidden Markov Models
  • Maximum Entropy Markov Models
  • Linear-Chain Conditional Random Fields
  • Recurrent Neural Networks

Data Integration

Network Analysis


Semantic Data

  • Ontologies
  • RDF - RDFS
  • OWL

Textual data and natural language processing

High Scalability - Data Parallelism

  • Comunication Patterns

Cloud Computing