Machine Learning With TensorFlow

This course is a hybrid course; you can participate either online via Zoom or in presence in Kiel (the number of places for presence participation is very limited).

Our courses are open to all and free of charge for students, pupils, trainees, founders, scientific staff, job seekers, refugees, and pensioners. For employees and self-employed persons, a fee of 200 euros is charged when acquiring a certificate of achievement. Employees and self-employed persons can apply for a scholarship via stipendium@opencampus.sh.

Your Background

You already have some programming knowledge and are interested in getting hands-on knowledge in how to train and use machine learning algorithms.
If you are interested in the theoretical backgrounds of machine learning, also check out our Deep Learning course.

The Course Content

This course is based on the online Coursera Specialization for the "TensorFlow Developer Professional Certificate" provided by deeplearning.ai. The instructors are Andrew Ng and Laurence Moroney. Andrew Ng was founding lead of the Google Brain Team and is a professor in computer science at Stanford University. Laurence Moroney is an AI developer at Google and author of a large variety of books.

The course will provide you with an introduction to machine learning and deep learning, teach you about computer vision using convolutional neural networks, natural language processing, and how to solve time series and forecasting problems. You will then apply the newly aquired knowledge to implement your own machine learning project in a team.

How It Works

The course will take place every Tuesday from 4 pm to 5:45 pm. Before the first meeting there will be a semesterkick-off on the 5th of November, in which you will get to know each other, and we will provide you with organizational details.

https://kurskickoff20-21.eventbrite.de

During the week you will be expected to work through the assigned online course content, which will take you between 3 to 5 hours each week, open questions considering the course content and possible additional implications will then be discussed in the weekly offline course on Tuesday. Towards the end of the semester you will then work in a team on your own machine learning project.
All needed software and online course content is free. For the practical assignments in the online course, however, it will be necessary to create a Google account.

The Formalities

In order to receive a certificate of achievement ("Leistungszertifikat") for this course, active participation is expected, and no more than two classes may be missed. The active participation is proven via the final presentation of your project by you and your team, and the delivery of a well documented project source code. The same conditions apply in order to receive ECTS.
In the online sessions it is necessary that you always provide your full name in Zoom so that your presence is registered on the EDU platform. No mere certificate of attendance will be issued for this course.

Further details may be given in the course.

If you want to learn more about your course lead, check out this link: https://www.linkedin.com/in/steffen-brandt/