Advanced Methods in Machine Learning

This course builds upon the fundamentals of machine learning, focusing on powerful and robust techniques for classification and regression. You will master Support Vector Machines and key ensemble methods like Random Forests and Boosting, while also covering essential skills like handling imbalanced data. The course concludes with a foundational, hands-on introduction to the basics of Deep Learning with PyTorch without going into details, designed to give you a starting point for further exploration in this field.

Target Audience

Learners with a basic understanding of machine learning who want to expand their toolkit with more advanced, widely-used techniques and gain a short, practical introduction to the world of deep learning.

Topics

• Robust Evaluation and Sampling: 
  • Cross-validation and bootstrapping to generate reliable model performance metrics.
  • Over- and under-sampling techniques to train effective models on imbalanced datasets.
• Ensemble Methods: 
  • Construction, application, and tuning Random Forest models for complex classification and regression tasks.
  • Boosting methods to create high-performance predictive models.
• Support Vector Machines: 
  • Principles of SVMs for linear and non-linear classification.
  • Fit and evaluation of SVMs on real-world data.
• A Brief Introduction to Deep Learning: 
  • Basic structure and components of a neural network.
  • Build and train simple Convolutional Neural Networks (CNN) using PyTorch for a classification task.

Methods

The course blends theory with hands-on application in Python. Each module introduces an advanced concept, followed by practical coding exercises that demonstrate its implementation and provide best-practice solutions.

Learning Goals

At the end of this course, you will be able to:

1. Master robust evaluation and sampling techniques 
2. Build and interpret powerful ensemble models
3. Apply support vector machines (SVMs)
4. Grasp the fundamentals of deep learning

Prerequisites

• Basic understanding of Python programming, e.g. Introduction to Python course.
• Basic knowledge of Machine Learning and model evaluation, e.g. Basic Methods in Machine Learning course.

Format

• Duration: either 2 full days or 4 half 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:
  • You can check the current dates and whether the courses are already fully booked here.
  • Registrations for these courses are exclusively possible via the provided homepage.

 * Links marked with * are only available for Helmholtz Munich staff.