Fall 2016

Data Science Seminar

Lecturers: Mourad Khayati

Teaching language: English

Level: MSc students

Academic year: Fall 2016

Overview

Structure

Evaluation and Expectations

Schedule

List of Papers


Overview

The seminar on data science involves presentations that cover recent topics on data science. The area of this year’s seminar is predictive analytics. In the scope of this seminar, we investigate papers that describe algorithms and techniques to perform prediction and trend analysis on different representations of data inputs, e.g., graphs, time series, etc.


Structure

The goal for the students is to learn how to critically read and study research papers, how to describe  a paper in a report, and how to present it in a seminar. Under supervision, students will select one paper to study, contrast and compare with related work. This seminar aims to help students to gather in-depth knowledge of an advanced topic and develop the skills required to describe a complex problem from the predictive analytics area in the form of both a presentation and a written report.

IMPORTANT NOTE: The papers will be distributed on a first come first serve basis.


Evaluation and Expectations

The final grade depends on the quality of the report, presentation, reproducibility experiments (if any) and active participation during the seminar. Each participant prepares a self contained report of max 10 pages and gives a presentation of 20 minutes. The report should describe in detail the proposed technique(s). The report might contain a small running example, counter example(s) and should explore the extreme cases where the proposed approach would perform best and worst. The reproducibility experiments consists on running the available code of the proposed system and making a 5 min demo about it.

Advices on how to:

IMPORTANT NOTE: Attendance is mandatory for the two class seminar sessions. The total number of participants will be limited to 10.


Schedule

Kickoff Meeting. Date: Tue, 27.09.2016, 14:00-16:00, room: A303
Setup and organization of seminar, and paper assignment

The presentation can be accessed through this link. 

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Date: Tue, 1.11.2016
Report deadline
Batch1

Date: Tue, 8.11.2016, all day, room: B312
Office meeting with students from
Batch1

First Seminar Session. Date: Tue, 15.11.2016, 14:15-18:00, room: A303

Presentations of Batch1

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Date: Tue, 29.11.2016
Report deadline of
Batch2

Date: Tue, 6.12.2016, all day, room: B312
Office meeting with students from
Batch2

Second Seminar Session. Date: Tue, 13.12.2016, 14:15-18.00, room: A303

Presentations of Batch2

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Date: Tue, 10.01.2017 extended to Fri, 13.01.2017
Deadline final Report of
Batch1 and Batch2



Paper Assignment

The papers will be distributed on a first come first serve basis. Please use the following link to select one paper among the list of papers.

Paper

Presentation Date

Presenter

First Report Deadline

Final Report Deadline

(1) Link Prediction in Graph Streams

15.11.2016

Michael Jungo

1.11.2016

10.01.2017

(2) GeoScope: Online Detection of Geo-Correlated Information Trends in Social Networks

15.11.2016

Tofunmi Ajayi

1.11.2016

10.01.2017

(3) Latent Space Model for Road Networks to Predict Time-Varying Traffic

15.11.2016

Antonios Chaidaris

1.11.2016

10.01.2017

(4)  SMiLer: A Semi-Lazy Time Series Prediction System for Sensors + reproducibility

15.11.2016

Michael Zbinden

1.11.2016

10.01.2017

(5)  Predictive Tree: An Efficient Index for Predictive Queries On Road Networks

15.11.2016

Sammer Puran

1.11.2016

10.01.2017

(6)  Dynamic and Robust Wildfire Risk Prediction System: An Unsupervised Approach

13.12.2016

Igor Dundic

29.11.2016

10.01.2017

(7) Online Anomaly Prediction for Robust Cluster Systems

13.12.2016

Michaël Diatta

29.11.2016

10.01.2017

(8) Deep Learning Architecture with Dynamically Programmed Layers for Brain Connectome Prediction

13.12.2016

Luka Hamza

29.11.2016

10.01.2017

(9) GLMix: Generalized Linear Mixed Models For Large-Scale Response Prediction

13.12.2016

Maryam Sadeghimehr

29.11.2016

10.01.2017

(10) Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering + reproducibility

13.12.2016

 Reto Schiegg

29.11.2016

10.01.2017