Intelligent Data Analysis

The rapidly escalating challenges in data science with respect to data size, dimensionality or heterogeneity highlighted the importance of the whole process of data analysis, including study design, data collection, data engineering, combination of a priori knowledge and data, combination of multiple inductive modules into a complex system and deriving optimal interventions. In parallel, the unprecedented challenges also renewed interest in complex inductive schemes, such as in learning of overall network models, causal systems models or in active and reinforcement learning.   The course provides a systematic overview both about intelligent methods used throughout the data analysis process and about intelligent, complex machine learning schemes used in modern data analysis. Unifying themes of this dual approach, are the Bayesian decision theoretic framework, the network and systems-based approaches, data and knowledge fusion, the use of ontologies and semantic technologies and active, online (reinforcement) learning, which integrate various phases and aspects of data analysis. The course also presents and discusses real-world applications, from the field of biomedicine, pharmaceutical research and system diagnostics.   The course is at the cross-road of statistics, big data analytics, artificial intelligence and machine learning. It is self-contained, but ideally complements earlier studies in these directions.   After accomplishing this course, you will be familiar with the following: (1)    Theoretical bases of induction. The engineering workflow of data analysis. (2)    Optimization, Bayesian model averaging and sensitivity analysis using resampling methods in data analysis. (3)    Semantic data repositories, data visualization, dimensionality reduction, data engineering/transformations using ontologies, data cleaning and imputation. (4)    Unsupervised learning: clustering, module learning, self-organizing maps, network science, metric learning. (5)    Supervised learning: decision trees, regression, kernel methods, multilayer perceptron, deep neural networks. (6)    Probabilistic graphical models: Bayesian networks, dynamic/temporal Bayesian networks. (7)    Reinforcement, active, budgeted and online learning. (8)    Knowledge and data fusion: ontologies, semantic technologies, linked open data.

D. J. Hand: Intelligent Data; Analysis; C.M. Bishop: Neural Networks for Pattern Recognition; Andrew Gelman: Bayesian Data Analysis; T.Hastie, R.Tibshirani, J.Friedman: The Elements of; Statistical Learning; R. G. Cowel: Probabilistic Networks and Expert Systems

basics of probability theory

Követelmények: a. Elkészítendő házi feladat. A vizsgára bocsátás feltétele a házi feladatnál a maximális pontszám legalább 50%-ának elérése. A megszerzett aláírás a tárgyfélévben és a továbbiakban a TVSz szerint érvényes. b. A vizsgaidőszakban: szóbeli vizsga. A kreditpont megszerzésének feltétele: legalább elég­séges vizsga. Pótlási lehetőségek: A házi feladatok - a kiadáskor rögzített - határidőre adandók be, pótlásuk a pótlási hét utolsó munkanapjáig lehetséges.