This course discusses basics of the knowledge discovery process, data mining, and provides a basic introduction to data science. The course focuses on two main aspects of knowledge discovery: mathematical tools and a well-defined and structured knowledge discovery process consisting of a number of interactive and iterative steps. The accompanying project (KU) concentrates on programming infrastructure for manipulating large-scale data. The examples that we use in the course are mainly text-mining and recommender examples

In recent years the amount of data that we produce increased dramatically. We already produce more data than we are able to store with the current technological solutions. Therefore, making sense out of these huge amount of data, or extracting useful, valid, understandable, and novel patterns from this data is of cruicial importance. Knowledge discovery, data mining, and data science are one of the approaches to tackle this problem. The other similar, but somewhat different approaches include database technology, machine learning, or statistics.

In this course we will investigate, analyze, and discuss a well-defined process for knowledge discovery in such a large data. Apart from the process we will also discuss the mathematics needed for data mining.

- Denis Helic (website)
- Roman Kern

Course topics include:

- Review of mathematics needed in data mining
- Knowledge discovery process
- Text classification and clustering
- Semantic analysis of text documents
- Recommender systems

In this course the students will:

- Learn about the mathematical basics of data mining algorithms
- Learn about the steps from a knowledge discovery process
- Learn about selected data mining algorithms

At the end of this course the students will know how to:

- Analyze and design a typical knowledge discovery project.

- 05.10.2017: Course organization, Introduction and Motivation
- 12.10.2017: Preprocessing
- 19.10.2017: Feature Extraction
- 09.11.2017: Feature Engineering
- 16.11.2017: Partial Exam 1 / Project presentations
- 23.11.2017: Data Matrices
- 30.11.2017: Principal Component Analysis and Singular Value Decomposition
- 07.12.2017: Recommender Systems: Matrix Factorization
- 14.12.2017: Topic Modeling and Non-negative Matrix Factorization
- 11.01.2018: Classification
- 18.01.2018: Clustering
- 25.01.2018: Evaluation
- 01.02.2018: Partial Exam 2 / Project presentations

- Review of probability theory and linear algebra
- Mining massive datasets
- Advanced Data Analysis from an Elementary Point of View
- Probability primer YouTube series
- Lecture slides "Mining Massive Datasets" from Stanford University
- Introduction to Information Retrieval
- Machine Learning Course by Andrew Ng
- Lecture Notes from 2017

- Probability Essentials by Jacod and Protter
- Machine Learning by Tom Mitchell

To refresh your knowledhe in linear algebra, probability theory and statistics submit the solutions (manually before the lecture) to these problems until 19.10.2017. This sheet will not be graded but you need to submit it!

There will be two partial examinations written within the classes. You will write the exam in the beginning of a lecture for 60 minutes. Each partial examination will have 3 questions with difficulty adjusted to solve both problems in approx. 50 minutes. You can get max 15 points for each question resulting in a total of 90 points.

Apart from the partial examinations there will be a standard written examination at the end of the course. 4 questions with max 20 points for each question. The total number of points that can be reached will be 80.

The grading scheme is as follows:

- 0-40 points: 5
- 41-50 points: 4
- 51-60 points: 3
- 61-70 points: 2
- 71-80 points: 1

The instructions for the practical project.