| Type | Advanced Lecture (6 ECTS) |
| Lecturer | Prof. Dr. Jilles Vreeken |
| vreeken (at) cispa.de | |
| Lectures | Thursdays, 10–12 o'clock (sharp) in 0.05 (CISPA, E9.1) and online via Zoom and YouTube |
| Registration | Not necessary, see below |
| Summary | In this advanced course we'll be investigating hot topics in machine learning and data mining that the lecturer thinks are cool. This course is for those of you who are interested in Machine Learning, Data Mining, Data Science – or, as the lecturer prefers to call it – Algorithmic Data Analysis. We'll be looking into how to determine causality from data, how to extract non-linear dependencies, discover significant and useful patterns from data, as well as how to gain insight into structured data. |
Students should have basic working knowledge of machine learning, data mining, and/or statistics, e.g. by successfully having taken courses such as Machine Learning, Probabilistic Graphical Models, Probabilistic Machine Learning, Elements of Machine Learning, etc.
The skills you will benefit most are critical thought and reading comprehension. We will practice these in the lectures and assignments.
There is no need to register for the course with the lecturer. The credentials to the Zoom meetings, YouTube stream, and necessary materials, will be shared in the first (publicly available) lecture.
As is usual, you will have to register for the exam via LSF. You can do so up to one week before the exam.
TADA will be taught hybrid. You are encouraged to attend the lectures in-person in the CISPA lecture hall (room 0.05 of E9.1), we will additionally stream the lectures to Zoom and YouTube. The Zoom meetings, YouTube streams, and edited videos will be linked in the schedule.
The credentials to access the course materials will be shared during the first lecture.
Students will individually do one assignment per topic – four in total. For every assignment, you will have to read one or more research papers and hand in a report that critically discusses this material and answers the assignment questions. Reports should summarise the key aspects, but more importantly, should include original and critical thought that show you have acquired a meta level understanding of the topic – plain summaries will not suffice. All sources you've drawn from should be referenced. The expected length of a report is 4 pages, but there is no limit.
The deadlines for the reports are on the day indicated in the schedule at 10:00 Saarbrücken standard-time. You are free to hand in earlier.
You will find some well-graded example reports here.
The assignments will be graded in scale of Fail, Pass, Very Good, and Excellent. Any assignment not handed in by the deadline is automatically considered Failed. You are allowed to re-do one Failed assignment: you have to hand in the improved assignment within two weeks. If the improved assignment is not at least a Pass, you are no longer eligible to take the exam.
Every Excellent gives you one bonus point, as do every two Very Good grades. Each bonus point improves a passing exam grade by 1/3, up to a maximum improvement of a full mark. For example, if you have two bonus points and you receive 2.0 from the final exam, your final grade will be 1.3. If you fail the final exam, you fail the course, irrespective of bonus points. Provided you are eligible to sit the final exam, previously Failed assignments do not reduce your final grade.
The final exams will be oral, and will cover all the material discussed in the lectures and the topics on which you did your assignments. The preliminary dates for the two exams are as follows. The main exam will most likely be in the week of August 7th. The re-exam will most likely in the week of October 10th. The exact time slot per student will be announced per email. Inform the lecturer of any potential clashes as soon as you know them.
All required and optional reading will be made available here. You will need a username and password that will be given out in the first lecture.
In case you do not have a strong enough background in data mining, machine learning, or statistics, these books [1,2,3,4] may help to get you on your way. The university library kindly keeps hard copies of these books available in a so-called Semesteraparat.
| [1] | Elements of Causal Inference. MIT Press, 2017. |
| [2] | All of Statistics. Springer, 2005. |
| [3] | Data Mining - The Textbook. Springer, 2015. |
| [4] | Patterns, Predictions, and Actions - A story about machine learning. Princeton University Press, 2022. |