Course Content
From medical decision support systems to automatic language translation, from sorting and prioritizing news on social networks to autonomous cars: Machine learning is woven into the fabric of daily life. Applying machine learning, data science aims to extract knowledge or insights from data.
The class will provide an introduction to data science and applied machine learning. For this, the programming language Python will be used (and taught). You will learn about the difference between supervised and unsupervised machine learning, and four machine learning tasks:
- Classification (e.g. k-NN, Decision Trees, Support Vector Machines)
- Regression (Linear Regression, Logistic Regression)
- Clustering (k-means)
- Dimensionality Reduction (PCA, t-SNE)
We will explore natural language processing for text mining and computer vision. Exploratory data analysis and evaluation, as an integral part of data science, will also be taught.
To succeed in this course, you have to watch the videos, do the exercises and applications, and work on your own project. Remember that these videos are not full-fledged lectures, they are a starting point for your own learning. Use material like the coursebook to learn more about the topics as we progress in the course.
This is a blended learning course. This means that we combine videos that we recorded with live tutorials in which you will be working on a real machine learning project.
All sessions in June and July will be done via Zoom. Whether we will meet in person in April or May will depend on the number of COVID-19 cases and the risk we (you as the students and us as instructors) are willing to take. We will survey you anonymously on your stance on these issues.
We will meet regularly (either in person or via Zoom), but most of the input will be provided as videos. This allows you to rewatch videos, watch them at different speeds, and discuss the videos with each other.
