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(INF342) -  ΓΙΑΝΝΗΣ ΝΙΚΟΛΕΝΤΖΟΣ - ΜΙΧΑΗΛ ΒΑΖΙΡΓΙΑΝΝΗΣ

Περιγραφή Μαθήματος

Course Schedule

LINK to data challenge: https://www.kaggle.com/c/inf342-datachallenge-2025

07/04/2025: 18:00 - 21:00 (Room: E3) -- Data Exploration, Feature engineering and practical Machine Learning for data challenges

14/04/2025: 18:00 - 21:00 (Room: E3) -- Traditional Text and Graph Mining, Introduction to the data challenge

28/04/2025: 18:00 - 21:00 (Room: E3) -- Machine/Deep learning for Text

05/05/2025: 18:00 - 21:00 (Room: E3) -- Machine/Deep learning for Graphs

12/05/2025: 18:00 - 21:00 (Room: E3) -- Text and graph concepts with worked examples using Python

19/05/2025: 18:00 - 21:00 (Room: E3) -- Text and graph concepts with worked examples using Python

Data Challenge Presentations

02/06/2025: 18:00 - 21:00 (Room: E3)

Course Overview

The field of data science has emerged in response to the significant increase in the availability of data that took place in the last decade. This course will introduce students to this rapidly growing field and will equip them with tools for working with data. The course will give students the chance to use these tools on real-world data. There will be no final exam. Instead, there will be a data challenge project (i.e. a data science problem organized as a competition). The challenge will let students go through the complete data science process. The final grade will be determined based on students' performance on the challenge, the approach they followed and their presentation.

Course Objectives

To develop an understanding of the key technologies in data science. To practice problem analysis and decision-making. Students will gain practical, hands-on experience through the challenge.

Key Outcomes

By the end of the course, students will have gained an understanding of data analysis techniques and how they can be applied to real-world datasets. Furthermore, students will be able to solve real-world data science problems using the principles and methods they have learned.

Topics to be Covered

• Data Preprocessing
• Feature Extraction/Engineering
• Supervised Learning
• Deep Learning methods (introduction)
• Text Mining
• Graph Mining

Instructor

Assistant Prof. Giannis Nikolentzos (https://users.uop.gr/~nikolentzos/)

Prof. Michalis Vazirgiannis (http://www.db-net.aueb.gr/michalis/)

Ημερομηνία δημιουργίας

Τετάρτη, 5 Απριλίου 2017