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.
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.
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 1: Introduction
- What is machine learning, and what can it do for us?
- Day 2: Optimization
- The derivative, gradients, optimization via gradient descent.
- Day 3: Linear Algebra:
- Matrix multiplication, singular value decomposition, Linear Regression.
- Day 4: Statistics
- mean and variance, correlation, gaussians.
- Day 5: Machine learning basics
- Overfitting and underfitting, classification, regression, k-nearest neighbours.
- Day 6: Support vector machines
- Linear separable, non-linear separable, kernel trick.
- Day 7: Decision trees and random forests:
- Decision trees, random forests, bias and variance problem, bagging.
- Day 8: Clustering and density estimation
- K-means clustering, Gaussian mixture models, expectation-maximization.
- Day 9: Principal component analysis (PCA)
- PCA for dimensionality reduction, PCA for compression and other applications.
- Day 10: Introduction to the HPC Systems at Uni Bonn.
- Day 11: Fully connected networks:
- The MNIST-data set, artificial neurons, forward and backward pass.
- Day 12: Convolutional neural networks:
- The convolution operation and convolutional neural networks.
- Day 13: Segmentation and optimization for deep learning:
- gradient descent with momentum, Adam, early stopping, regularization.
- Day 14: Interpretability:
- visualization of linear classifiers, saliency maps, integrated gradients
- Day 15: Sequence models:
- Transformers, Long-Short-Term-Memory, text-based language models.
See you during the course,
Your lecturers, Elena and Moritz.
We thank the state of North Rhine-Westphalia and the Federal Ministry of Education and Research for supporting this project.