Welcome
Welcome! In this course you will learn about the state of the art of Machine Learning and also gain practice implementing and deploying machine learning algorithms.
The aim of Machine Learning is to build computer systems that can adapt to their environments and learn form experience. Learning techniques and methods from this field are successfully applied to a variety of learning tasks in a broad range of areas, including, for example, spam recognition, text classification, gene discovery, financial forecasting. The course will give an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as linear regression and classification and ending up with topics such as kmeans and Expectation Maximization. The course will give you the basic ideas and intuition behind these methods, as well as a more formal statistical and computational understanding. You will have an opportunity to experiment with machine learning techniques in R and apply them to a selected problem.
Course Schedule
| Session | Type | Duration | Content |
|---|---|---|---|
| Lecture 1 | Online | 1.5h | Introduction + Regression |
| Session 1 | PW | 3h | |
| Session 2 | PW | 3h | |
| Lecture 2 | Online | 1.5h | Classification |
| Session 3 | PW | 3h | |
| Session 4 | PW | 3h | |
| Session 5 | PW | 3h | |
| Quiz | DVO | 30mins | Evaluation: Quiz |
| Lecture 3 | Online | 1.5h | Dimensionality Reduction |
| Session 6 | PW | 3h | |
| Lecture 4 | Online | 1.5h | Clustering |
| Session 7 | PW | 3h | |
| Session 8 | PW | 3h | |
| Session 9 | PW | 3h | Evalution: Hackathon |