The course "Advanced Methods in Machine Learning" expands on foundational machine learning techniques by introducing more advanced methods such as Support Vector Machines, ensemble methods like Random Forests and Boosting, and basic Deep Learning with convolutional networks. Participants will also learn about sampling methods, model evaluation, and handling imbalanced datasets, gaining practical experience with Python and PyTorch for real-world applications.
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
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.
Learning goals
Understand Different Resampling Techniques
- Explain features and usage of cross-validation.
- Describe the advantages of bootstrapping.
- Discuss sampling techniques handling imbalanced data sets.
Understand and Apply Ensemble Methods
- Extend decision trees to random forests and discuss the usage.
- Describe boosting algorithms and their benefits and applications.
Introduce Advanced Classification Methods
- Understand Support Vector Machines and describe their limitations and advantages.
- Explore other methods used for classification.
Provide an Overview on Deep Learning
- Gain an initial understanding of deep learning, with hands-on practice using convolutional networks in PyTorch.
Implement Machine Learning Techniques Using Python
- Apply learned machine learning algorithms and techniques in Python to solve real-world data analysis problems.
Course date
Register now: May 15–16 and 22–23, 2025
For more information on how to register, please follow the link on the course date.
Prerequisites
- Basic understanding of Python programming, as covered in the courses “First steps in Python” and “Data processing with Pandas & Data visualization in Matplotlib”.
- Basic knowledge of Machine Learning and model evaluation, as covered in the course “Machine Learning 1”.
Target group
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.
This course is free of charge.
