The lecture gives an introduction into probability theory, statistics, information theory, data (pre-)processing and indexing techniques, efficient similarity analysis, as well as linear and nonlinear predictive models. In addition to last year's lecture, we also introduce relevant concepts of fairness and explainability in machine learning.
The lecture will be held in english and online via Zoom. The number of participants is limited to 70 in order to enable a written examination with physical presence under the current circumstances. Please register to the lecture via Ilias (link below; first come, first serve).
Although well-trained complex Machine Learning models may provide accurate predictions, they often suffer from being difficult to explain. In this seminar, we will explore how interpretablity and transparency for such models can be achieved, while simultaneously producing human-understandable and useful explanations.
Preliminary Schedule & Setup
The intital meeting takes place on Monday, 27. April 2020. We plan to conduct the meeting online, using zoom, microsoft teams or another online platform. Please use Ilias to register for the course since we will disseminate all required information using this platform. In order to successfully take part in the seminar, we ask students to complete small research projects. In June, before the exam period starts, students are asked to present their findings. The first meeting provides more information about the details of the research projects, supervision and organizational aspects.
The lecture gives an introduction into probability theory, statistics, information theory, data (pre-)processing and indexing techniques, efficient similarity analysis, classification, clustering and probabilistic inference in graphical models. The lecture and exercise sessions will be held in english.
The seminar offers an introduction to the new and fast developing field of algorithmic fairness. During the seminar, we will discuss the latest developments, particularly focusing on the latest research results. We expect seminar participants to have basic knowledge of probability theory and machine learning. Moreover, we assume that seminar participants have good coding skills and a natural interst in socio-technical questions as most of the presented ideas and algorithms are being applied to contexts in which human descion makers are either replaces or supported by machine learning algorithms (e.g. credit scoring, college admissions, recidivism prediction).
- Fairness Definitions
- Causality Based Fairness Defintions
- Impossibility Theorems
- Learning Fair Representations (pre-processing)
- Post-processing methods
- ML algorithms subject to fairness constraints
The lecture gives an introduction into advanced methods for discovering patterns in large data sets and involves various techniques from the areas of machine learning, statistics, probability theory, and information systems.
- Basics of Probability Theory and Statistics
- Basics of Information Theory
- Data Preprocessing
- Data Indexing for Efficient Similarity Search
- Supervised Learning: Introduction To Classification
- Linear and Non-Linear Classifiers
In many practical scenarios, the abundance of data provided by many information sources makes it probable to find that different sources provide partially or completely different values for the same data item. And sometimes the information sources do not provide any values at all. In this seminar, we discuss probabilistic techniques for improving the quality of the generated data by inferring the correct values for the provided data items and the reliability of the information sources.
Lecturer: Gjergji Kasneci