This page is located in archive. Go to the latest version of this course pages. Go the latest version of this page.

xp36vpd -- Selected parts of data mining

Data mining aims at revealing non-trivial, hidden and ultimately applicable knowledge in large data. This course focuses on two key data mining issues: data size and their heterogeneity. When dealing with large data, it is important to resolve both the technical issues such as distributed computing or hashing and general algorithmic complexity. In this part, the course will be motivated mainly by case studies on web and social network mining. The second part will discuss approaches that merge heterogeneous prior knowledge with measured data. Bioinformatics will make the main application field here. It is assumed that students have completed at least some of the master courses on Machine Learning and Data Analysis (B4M36SAN, B4M46SMU, BE4M33SSU).

The course will take a form of reading and discussion group. Each student gives two 1 hour lectures, followed by a 30 min discussion. One of the lectures shall be DM general (MMDS book chapters, recent tutorials at major ML/DM conferences, etc.), the second one can present your research (if DM related) or a DM topic that is closely related to your research or research interests. Each student is supposed to read a review paper recommended for the topic before presentations of the other students.

Go beyond the literature, provide own insight, offer own illustrative examples, etc.

The students who do not present are supposed to read recommended reading and prepare a couple of questions before the class. The questions will be discussed during or after the talk.

Fall 2018

L Date Presents Contents Reading Talk, other links
1 Oct 4 JK, FZ Course overview, introduction, research interests.
2 Oct 11 JK, FZ Preparation of the first round of presentations.
3 Oct 18 JK, FZ Preparation of the first round of presentations.
4 Oct 26 Maria Rigaki Adversarial ML and Generative Adversarial Networks AML review AML intro, GANs, Talk
5 Nov 2 Milan Němý Deep reinforcement learning Deep RL AlphaGO, Talk
6 Nov 9 Jan Brabec Non-linear dimensionality reduction – UMAP DR review UMAP, tSNE, Talk
7 Nov 16 Filip Paulů On-line learning Nonstat ConceptDrift, OnlineDeep, Talk
8 Nov 23 Matej Uhrín Beyond Manual Tuning of Hyperparameters bayesOpt HyperReview, Talk
9 Nov 30 Maria Rigaki Gradient boosting trees GBMTutorial Kaggle blog, XGBoost, Talk
10 Dec 7 Milan Němý Image segmentation with convolutional neural nets CNNs for BioImages DeepNetEMI, Talk
11 Dec 14 Filip Paulů Analogous Neural Networks HWANN NeuromorphicComputing, Talk
12 Jan 4 Jan Brabec Bad practices in ML TroublingMLTrends ClassImbalance, UsefulToKnow, Talk
13 Jan 11 Matej Uhrín Learning for sport bets Kelly crit Bernoulli, Talk
14 Jan 18 JK, FZ zkouška


Evaluation, requirements

  • every student must give his talks (the principle requirement in this type of course),
  • attendance and active discussion at presentations of other students,
  • pass the exam, i.e., prove the knowledge of basic concepts presented during the course.
courses/xp36vpd/start.txt · Last modified: 2019/01/16 09:34 by klema