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Taken your first steps in ML and thirsty for becoming a fully equipped practitioner? Then this course is perfectly right for you! You will learn+build all relevant NN Architectures with the best tools
Learning Goals:
- PyTorch and PyTorch Lightning
- Understand and deepen all Machine Learning concepts
- Understand and build CNNs, RNNs and Transformers
- Build your own end-to-end project on Kaggle
- Become competitive and employable
- Learn and use state-of-art tools like Hugging Face
What you get
The course will provide you with knowledge and experience about recent and advanced machine learning models. You will train and use models from the first week of the course on to very sophisticated models at the end of the course.
You will apply your newly acquired knowledge in a team to a Kaggle competition and hopefully achieve interesting and impressive results.
This course is a mix from a lot of content, some parts of it are based on the Coursera Course on Machine Learning https://www.coursera.org/learn/machine-learning-duke.
How it works
The course will take place every Thursday from 6 pm to 7:45 pm. The mode will be hybrid i.e. you can choose if you want to participate online or in person in Kiel.
During the week you will be expected to work through the assigned online course content, which will take you between 5 to 10 hours each week. Questions considering the course content and possible additional implications will then be discussed in the weekly course meeting on Thursday. There will be certain homework submissions during the course which you have to do for passing the course. Towards the end of the semester you will then work in a team on a 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 and Kaggle account.
Before the first course meeting there will be a Course Kick-Off Event on Oktober 20: In the Kick-Off you can meet your course instructor and ask questions about the participation in the course. The attendance at the Kick-Off is not mandatory but recommended for all participants. You register here: https://edu.opencampus.sh/course/445
What you should bring
You already have some knowledge about Machine Learning (e.g. followed our course Introduction to Tensorflow) and are interested in learning more. Important architectures like CNNs and Transformers will be (re)introduced and in the end thoroughly understood. Particularly in this course you should be motivated to bring your own contribution – its a great opportunity to learn and discuss state-of-the-art methods in the field.
Workload
You should plan to spend about 10 hours per week for the course including our weekly course session. If you are really good and have solid foundations you might go down to 6-7 hours. This is the absolute minimum you should expect. Google and peers do not pay entry salaries over a 100k because Machine Learning is so particularly easy. You have to spend hard work in the course but I promise it is worth it.
The Formalities
In order to receive a certificate of attendance (Leistungszertifikat) for this course, active participation is expected, and no more than two classes may be missed. The active participation is proven via the homework submisson and 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.
In Kooperation mit:
