|Course type:||Flipped-classroom Lecture + Exercise|
|Time:||Monday 14:00 - 16:00|
|Location:||SR 01-009/13 (G.-Köhler-Allee 101)|
|Organizers:||Frank Hutter, André Biedenkapp, Noor Awad, Heri Rakotoarison,Neeratyoy Mallik, Eddie Bergman|
Applying machine learning (ML) and in particular deep learning (DL) in practice is a challenging task and requires a lot of expertise. Among other things, the success of ML/DL applications depends on many design decisions, including an appropriate preprocessing of the data, choosing a well-performing machine learning algorithm and tuning its hyperparameters, giving rise to a complex pipeline. Unfortunately, even experts need days, weeks or even months to find well-performing pipelines and can still make mistakes when optimizing their pipelines.The course will discuss meta-algorithmic approaches to automatically search for, and obtain well-performing machine learning systems by means of automated machine learning (AutoML). Such AutoML systems allow for faster development of new ML/DL applications, require far less expert knowledge than doing everything from scratch and often even outperform human developers. In this lecture, you will learn how to use such AutoML systems, to develop your own systems and to understand ideas behind state-of-the-art AutoML approaches.
We strongly recommend that you know the foundations of
- machine learning (ML)
- and deep learning (DL)
We further recommend that you have hands-on experience with:
- Python (3.8+)
- machine learning
- deep learning
The participants should have attended at least one other course for ML and DL in the past.
The lectures are partitioned in several parts, including:
- Hyperparameter Optimization
- Bayesian Optimization for Hyperparameter Optimization
- Neural Architecture Search
- Dynamic Configuration
- Analysis and Interpretability of AutoML
- Algorithm Selection/Meta-Learning
The course will be taught in a flipped-classroom style. We will meet weekly once for a combined Q/A session and an exercise. Roughly every week, there will be a new exercise sheet. Most exercises will be practical, involve programming in python and teamwork (teams of up to 3 students!) so that you learn how to apply AutoML in practice.
Lecture/Exercise: Monday 14 - 16
The material is publicly available via the AI-Campus platform. Please register on AI-Campus to access the materials. Grading of the exercises will be done via GitHub classroom. The links to access the GitHub classroom repositories will be made available at the beginning of the semester. (The password to that page will be announced in the first session). The course will be taught in English.