The Mining of Massive Datasets Challenge

News

18-02-2019	A sheet with final evalution can be found here.
11-01-2019	The deadline for the final report submission has been extended to January 21, 2019
11-01-2019	Presentations of the final reports will take place on:
	- Wednesday, January 23, 15:10 (room L128 BT)
	- Thursday, January 24, 13:30 (room L125 BT)
02-11-2018	The revised schedule of the students' talks can be found below in Section "Schedule of lectures"
30-10-2018	The Data Ninja competition: the official launch of the competition (Wednedsay, Nov. 7, 15.10, room L128 BT)
30-10-2018	The competition web page has been launched.
17-10-2018	Office hours cancelled on Thursday, October 18, because of the PP-RAI conference (please email me with you want to meet).
10-10-2018	The challenge has begun :)

The aim of the course: To learn how to deal with real-life massive data (or to win a data mining competition).

The scope of the course: Application of data mining algorithms to real-life massive data:

Lecture: Wednesday, 15:10, room L128 BT
Project/Labs: Monday, 16:50, room 44 CW, Tuesday 16:50 room 44 CW.
Office hour: Thursday, 10:00-12:00, room no. 2 CW. (Institute of Computing Science)

Invited lecture(s)
Students' presentations about their solutions in the competition.
Students' talks on selected topics (time slots: Nov. 14, Nov. 21, Dec. 12, Dec. 19, Jan. 9)
Lectures on demand (video lectures, lecture on a given topic)

11-10-2018	The mining massive data sets challenge [pdf]
07-11-2018	Launching of the "Data Ninja" competition
14-11-2018	Students' talk: Natural language processing (Zofia Długosz, Michał Rajewski) [pdf]
	Students' talk: Deep networks: TensorFlow (Jakub Białek, Patryk Kuśmierkiewicz) [pdf]
	Student's talk: Building Autoencoders with Keras Library (Adam Ćwian, Filip Grześkowiak) [pdf]
21-11-2018	Students' talk: Deep networks: Torch (Jan Andraszyk, Łukasz Jędryczka) [pdf]
	Students' talk: Deep Networks: Theano (Jan Cofta, Konrad Śniatała) [pdf]
12-12-2018	Students' talk: Deep reinforcement learning (Jan Klimaszyk, Marcin Sendecki) [pdf]
	Students' talk: Positive-unlabelled learning (Łukasz Osiński, Mateusz Lewandowski) [pdf]
	Students' talk: Active learning (Damian Szalbierz, Kamil Szmajdziński) [pdf]
19-12-2018	Students' talk: OpenAi Gym (Paweł Szudrowicz, Dominik Białecki) [pdf]
	Students' talk: Microsoft Azure for Big Data and Machine Learning (Grzegorz Kotlarz, Radosław Waberski) [pdf]
	Students' talk: Zero-shot learning (Dawid Ciok, Adam Kokosza) [pdf]
09-01-2019	Students' talk: ML Packages: MLib (Amadeusz Mileszko, Filip Philavong) [pdf]
	Students' talk: Multi-label classification (Patryk Smól, Wojciech Bełka) [pdf]
	Students' talk: Deep networks: TensorFlow (Wojciech Działowski) [pdf]
16-01-2019	Presentation of students' reports

For more information check: dataninja.olx.pl

Meetings: hints and tips how to win the challenge, open discussion on possible solutions, faced problems, and used algorithms.

Information about project's meetings can be found here.

Lecture:

Report: your approach to win the competition:	70 % of points (min. 50%)
Seminar students' talk :	30 % of points
Top score in the challenge :	Bonus of 10 percent points

Scale: