- linear algebra

- basic programming techniques in any programming language 

- Introduction and important assets of data mining and data science
- Practical application of data science through important tools

- Linear and polynomial, one and multidimensional regression and optimization: gradient descent and least squares

- Supervised learning (classification): nearest neighbour methods, decision trees, logistic regression, non-linear classification, neural networks, support vector networks, timeseries classification and dynamic time warping

- Advanced classification methods: semi-supervised learning, multi-class classification, multi-task learning, ensemble methods: bagging, boosting, stacking, ensemble of classifiers by Dietterich

- Evaluation of classifiers: cross-validation, bias-variance trade-off

- Clustering: k-means (k-medoid, FurthestFirst), hierarchical clustering, Kleinberg's impossibility theorem, internal and external evaluation, convergence speed

- Principal component analysis, low-rank approximation, collaborative filtering and applications (recommender systems, drug-target prediction)

- Density estimation and anomaly detection

- Frequent itemset mining

- Biomedical data processing (next-generation sequencing, gene expression, biomedical timeseries) and mining

- Additional applications and problems: preprocessing, scaling, overfitting, hyperparameter optimization, imbalanced classification

- Tools: Octave/Matlab, Python, R, Hadoop

- during the semester: 5 homeworks

- final: oral exam 

Pang-Ning Tan, Michael Steinbach, Vipin Kumar:

Introduction to Data Mining 

http://www-users.cs.umn.edu/~kumar/dmbook/index.php

Bodon Ferenc, Buza Krisztián: Adatbányászat, elektronikus jegyzet

http://www.cs.bme.hu/~buza/pdfs/adatbanyaszat-cover.pdf

Dr. Buza Krisztián tudományos munkatárs, MTA-TTK

 Bálint Daróczy  HAS Institute for Computer Science and Control