Machine Learning and Data Mining[go to overview]
Winter Term 2020 / 2021
The course “Machine Learning and Data Mining (MLDM)” covers the fundamentals and basics of machine learning and data mining. The course provides an overview of a variety of MLDM topics and related areas such as clustering and classification.
Excited to participate? Then, you need to:
- Register to the lecture in KLIPS. Mandatory for all students who want to participate.
- Add yourself into a group for working on mandatory assignments in Teams until 11th November. Applies only for those students who take the course the first time or did not gather the exam admission in the last winter term.
- Register for the course in OLAT. You will need a passcode for the registration in OLAT. We will send you this passcode via E-Mail before the first lecture. The E-Mails will be sent to all students who have registered for the lecture in Klips, see 1.
Please use the forum on the OLAT platform for general questions about the course. You may send E-Mails only for urgent issues or when it is about private data.
Info: Exam admissions from the Machine Learning and Data Mining course of the winter term 2019/2020 are still valid, regardless whether one participated in an exam. Thus, students who attended the course one year ago and scored at least 60% of the points in the assignments must not register for an assignment group again.
Master and Bachelor students in:
- Web Science
- Computer Science
- Computational Visualistics
- Business Informatics
Slides, lecture recordings and additional material will be provided along with the progress of the lecture. Below, the table of dates and topics is subject to changes.
|4th November||Introduction||Zeyd Boukhers||See OLAT|
|11th November||Data Preprocessing||Raphael Menges||See OLAT|
|18th November||Data Transformation||Raphael Menges||See OLAT|
|25th November||Clustering 1||Raphael Menges||See OLAT|
|2nd December||Clustering 2||Raphael Menges||See OLAT|
|9th December||KNN and Naive Bayes||Tjitze Rienstra||See OLAT|
|16th December||Decision Tree||Tjitze Rienstra||See OLAT|
|6th January||Linear Classification||Zeyd Boukhers||See OLAT|
|13th January||Neural Network||Zeyd Boukhers||See OLAT|
|20th January||Deep Learning||Zeyd Boukhers||See OLAT|
|27th January||Hidden Markov Chain||Tjitze Rienstra||See OLAT|
|3rd February||See OLAT|
|10th February||See OLAT|
Tutorials will discuss the solutions to the last assignment and discuss the exercises of the current one. Solutions will not be uploaded!
|12th November||Organizational Notes||Raphael Menges||See OLAT|
|19th November||Machine Learning Fundamentals||Raphael Menges||See OLAT|
|26th November||Data Preprocessing||Raphael Menges||See OLAT|
|3rd December||Data Transformation||Raphael Menges||See OLAT|
|10th December||Clustering||Raphael Menges||See OLAT|
|17th December||KNN||Tjitze Rienstra||See OLAT|
|7th January||Naive Bayes||Tjitze Rienstra||See OLAT|
|14th January||Decision Tree||Tjitze Rienstra||See OLAT|
|21st January||SVM||Zeyd Boukhers||See OLAT|
|28th January||Neural Network||Zeyd Boukhers||See OLAT|
|4th February||Deep Learning||Zeyd Boukhers||See OLAT|
|11th February||Hidden Markov Chain||Tjitze Rienstra||See OLAT|
Assignments will be provided along with the progress of the lectures.
Please form groups of 3 to 4 people to work on the assignments here, until 11th of November! The assignments are graded before the next tutorial and it is mandatory to reach 60% of the points in total over all assignments to be allowed to participate in the exam. E.g., if there are 10 assignments each 100 points, you need in total at minimum 600 points in sum over all assignments to participate in the exam.
|Release Date (may be released earlier)||Deadline (9:00 AM!)||Topic||Sheets|
|9th November||16th November||Machine Learning Fundamentals||See OLAT|
|16th November||23rd November||Data Preprocessing||See OLAT|
|23rd November||30th November||Data Transformation||See OLAT|
|30th November||7th December||Clustering||See OLAT|
|7th December||14th December||KNN||See OLAT|
|14th December||21st December||Naive Bayes||See OLAT|
|4th January||11th January||Decision Tree||See OLAT|
|11th January||18th January||SVM||See OLAT|
|18th January||25th January||Neural Network||See OLAT|
|25th January||1st February||Deep Learning||See OLAT|
|1st February||8th February||Hidden Markov Chain||See OLAT|