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 email@example.com.
Note: There may be some small changes in the single sessions. The topics and dates won't change, but there may be more or less space allocated for something. This will be cleared in the following weeks and explained further if needed in the first session.
What you get
This course is based on the first three courses of the online Coursera Specialization "Deep Learning" provided by deeplearning.ai. The instructor is Andrew Ng, co-founder of Coursera, founding lead of the Google Brain Team, and professor in computer science at Stanford University.
The course will provide you with an introduction to machine learning and deep learning, teach you the theoretical foundation behind neural network and how to implement them. We will discuss and implement step by step the basic blocks of deep learning: forward and backpropagation, neuron and layers, activation functions, optimizer, parameters and more. You will then apply the newly acquired knowledge to implement your own deep learning project in a team which you will present at the end of the course.
This course is part of the opencampus.sh Machine Learning Degree. Participants of the program for the Machine Learning Degree get preferred access to this course. Find more information on the opencampus.sh Machine Learning Degree page.
All needed software and online course content is free. As Germany's first Coursera for Campus partner, opencampus.sh will in particular provide you with full access to the Coursera online learning platform.
What you should bring
You have some basic knowledge about programming and linear algebra (working with vector and matrices) and are interested in getting in-depth knowledge on how to implement machine learning algorithms.
If you are interested in machine learning more from a practical perspective, check out also our Machine Learning with Tensorflow course. Also, you should calculate sufficient time. During the week you will be expected to work through the assigned online course content and complete home assignments, for which you should calculate 4 to 8 hours each week. With the start of your project (after four weeks), you will in addition need some hours more to work on that (the assignment will decrease in the last weeks to give you more time to work on the project).
In order to receive a certificate of achievement ("Leistungszertifikat"/ ECTS) for this course, active participation is expected, no more than two classes may be missed, and you have to conduct a practice project in a team of 2 to 4 persons. At the end of the course the project has to be presented and a well documented project source code has to be submitted.
Before the first meeting there will be a Semester Kick-Off Event open for all courses (not only for this one). In the Kick-Off you will meet your course instructor, can ask questions about the participation in the course, and get helpful information to prepare for the start of the course. The attendance at the Kick-Off is not mandatory but recommended for all participants.
The course will be streamed online via Zoom.