Three weeks, 15 days, a lecture and exercises every day. The three-week course takes place from 9:00-17:00 at the University IT and Data Center (Hochschulrechenzentrum HRZ). The course structure is 90 minutes of lecture 90 min exercises, followed by 4 hrs of programming under guidance from the tutors.

Members of the University of Bonn can register via ecampus.

Prerequisites: Programming in Python. If you are not yet familiar with python, please consult https://docs.python.org/3/tutorial/ before the first session.

• Day 01:
  • Introduction
    • What is machine learning, and what can it do for us?
    • recording, exercise, slides
      
  • Optimization
    • The derivative, gradients, optimization via gradient descent.
    • recording, exercise, slides
• Day 02: Linear Algebra:
  • Matrix multiplication, singular value decomposition, Linear Regression.
  • recording, exercise, slides
• Day 03: Statistics
  • mean and variance, correlation, gaussians.
  • recording, exercise, slides

• Day 04: Machine learning basics
  • Overfitting and underfitting, classification, regression, k-nearest neighbours.
  • recording, exercise, slides
• Day 05: Support vector machines
  • Linear separable, non-linear separable, kernel trick.
  • recording, exercise, slides
• Day 06: Decision trees and random forests:
  • Decision trees, bias and variance problem, bagging, random forests.
  • recording, exercise, slides
• Day 07: Clustering and density estimation
  • K-means clustering, Gaussian mixture models, Expectation-Maximization.
  • recording, exercise, slides
• Day 08: Principal component analysis (PCA)
  • PCA for dimensionality reduction and other applications.
  • recording, exercise, slides

• Day 09: Introduction to the HPC Systems at Uni Bonn.

• Day 10: Fully connected networks:
  • The MNIST-data set, artificial neurons, forward and backward pass.
  • recording, exercise, slides
• Day 11: Convolutional neural networks:
  • The convolution operation and convolutional neural networks.
  • recording, exercise, slides
• Day 12: Optimization for deep learning:
  • Gradient descent with momentum, Adam, early stopping, regularization
  • recording, exercise, slides
• Day 13: Segmentation:
  • Semantic Segmentation, U-Net, Intersection-over-Union, Focal loss
  • [recording], exercise, [slides]
• Day 14: Interpretability:
  • visualization of linear classifiers, saliency maps, integrated gradients
  • recording, exercise, slides
• Day 15: Sequence models:
  • Transformers, Long-Short-Term-Memory, text-based language models.
  • recording(update coming soon), exercise, slides

See you during the course,

Your lecturers, Elena, Lokesh, and Moritz.

We thank the state of North Rhine-Westphalia and the Federal Ministry of Education and Research for supporting this project.