Advanced Methods in Machine Learning
Course description
Requirements:
- Basic understanding of Python programming, as covered in the Introduction to Python course.
- Basic knowledge of Machine Learning and model evaluation, e.g. Introduction to Machine Learning course.
Course overview:
In this course, we go beyond the most basic approaches used in Machine Learning for classification and regression. We will explore Support Vector Machines, ensemble methods like Random Forests and Boosting, and introduce the fundamentals of Deep Learning using convolutional networks. Furthermore, we also cover sampling techniques for robust model evaluation, measuring estimation confidence, and handling imbalanced datasets. By the end, you will have an overview of some of the most important techniques in Machine Learning, can apply these methods in real-world scenarios, and have a basic understanding how Deep Learning can be applied using PyTorch.
Topics:
- Sampling methods
- Cross-validation
- Bootstrapping
- Over- and undersampling for imbalanced datasets
- Ensemble methods
- Random Forests
- Boosting
- Support Vector Machines
- Basics in Deep Learning
Target Audience:
This course is designed for learners who have a foundational understanding of Python and Machine Learning, and are eager to get a general understand of more advanced classification and regression techniques and a basic introduction to Deep Learning.
Methods:
The course consists of theoretical lessons on machine learning tools and how to apply Machine Learning techniques. Theoretical lessons will be followed by hands-on examples with best-practice solutions in Python.
Format:
- Duration: 2 Days
- Language: English
- This course will be offered either on campus (in person), or online
- For online courses we use the software Zoom.
Dates and Application:
- Courses provided for Helmholtz Munich:
- You can check the current dates and whether the courses are already fully booked here*.
- Please read the corresponding FAQ* before applying via the forms of the HR Development department*.
- Courses provided for HIDA:
* Links marked with * are only available for Helmholtz Munich staff.