{"id":6108,"global_id":"blog.rwth-aachen.de\/itc-events\/en?id=6108","global_id_lineage":["blog.rwth-aachen.de\/itc-events\/en?id=6108"],"author":"1859","status":"publish","date":"2022-09-19 12:33:43","date_utc":"2022-09-19 10:33:43","modified":"2022-09-19 13:59:35","modified_utc":"2022-09-19 11:59:35","url":"https:\/\/blog.rwth-aachen.de\/itc-events\/en\/event\/training-introduction-to-machine-learning-deep-learning\/","rest_url":"https:\/\/blog.rwth-aachen.de\/itc-events\/en\/wp-json\/tribe\/events\/v1\/events\/6108","title":"Training: Introduction to Machine Learning & Deep Learning","description":"

Name: <\/strong>Training: Introduction to Machine Learning & Deep Learning<\/p>\n

Event type: <\/strong>Workshop<\/p>\n

Date:\u00a0<\/strong>October 19 to 20, 2022
\n<\/strong><\/p>\n

Time:<\/strong> 9am – 1pm (2\u00d74 Hours, 3 hours of presentation, 4 hours of hands-on live exercises, and a discussion)<\/p>\n

Format:\u00a0<\/strong>Online
\n<\/strong><\/p>\n

Target Audience: <\/strong>ML\/DL beginners, Scientists who want to apply ML methods
\n<\/strong><\/p>\n

Capacity: <\/strong>Unlimited<\/p>\n

Requirements (required knowledge\/experience):\u00a0<\/strong>Laptop and a working internet connection<\/p>\n

Contact Person: <\/strong>Zahra Sadeghibogar<\/a> and Jonas Seng<\/a><\/p>\n

*More information<\/a>*\u00a0<\/strong>
\n<\/strong><\/p>\n

\n

 <\/p>\n

Desciption\u00a0\u00a0<\/span><\/h3>\n

Not only in economics Machine- and Deep-Learning (ML\/DL) are inherently used to solve highly complex problems in a datadriven way, but also the scientific community has many use-cases in which ML\/DL are useful, e.g. to discover hidden patterns or replace computationally heavy simulations with data-driven approaches.<\/p>\n

This workshop will introduce the basics of ML and DL in a theoretical and practical way with the State of Art technologies. The participants will learn how to design ML-models by themselves and will learn about possible pitfalls when applying ML in the real world.<\/p>\n

\n

 <\/p>\n

Agenda\u00a0\u00a0<\/span><\/h3>\n

Day 1 – October 19, 2022<\/strong><\/span>\u00a0<\/span>
\n9:00 \u2013 9:15<\/strong>
\nWelcome<\/p>\n

9:15 \u2013 9:35<\/strong>
\nSetup<\/p>\n

9:35 \u2013 10:00<\/strong>
\nData Preparation (Presentation)<\/p>\n

10:00 \u2013 10:30<\/strong>
\nData Preparation (Hands On)<\/p>\n

10:30 \u2013 10:45<\/strong>
\nBreak<\/p>\n

10:45 \u2013 11:15<\/strong>
\nRandom Forests (Presentation)<\/p>\n

11:15 \u2013 11:45<\/strong>
\nRandom Forests (Hands On)<\/p>\n

11:45 \u2013 12:15<\/strong>
\nGeneralized Linear Models (Presentation)<\/p>\n

12:15 \u2013 12:45<\/strong>
\nGeneralized Linear Models (Hands On)<\/p>\n

12:45 \u2013 13:00<\/strong>
\nWrap Up<\/p>\n


\nDay 2 – October 20, 2022<\/strong>\u00a0<\/strong><\/span><\/p>\n

9:00 \u2013 9:15<\/strong>
\nWelcome<\/p>\n

9:15 \u2013 9:45<\/strong>
\nNeural Networks (Presentation)<\/p>\n

9:45 \u2013 10:15<\/strong>
\nNeural Networks (Hands On)<\/p>\n

10:15 \u2013 10:45<\/strong>
\nHyperparameter Tuning (Presentation)<\/p>\n

10:45 \u2013 11:15<\/strong>
\nHyperparameter Tuning (Hands On)<\/p>\n

11:15 \u2013 11:30<\/strong>
\nBreak<\/p>\n

11:30 \u2013 12:00<\/strong>
\nPitfalls of Neural Networks (Presentation)<\/p>\n

12:00 \u2013 12:10<\/strong>
\nWrap Up<\/p>\n

12:10 \u2013 13:00<\/strong>
\nDiscussion & 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