Machine Learning and Data Mining[zur Übersicht]
Wintersemester 2020 / 2021
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.
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 here 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.
For inter-student communication, please use the newsgroup infko-mldm.
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|
|11th November||Data Preprocessing||Raphael Menges|
|18th November||Data Transformation||Raphael Menges|
|25th November||Clustering 1||Raphael Menges|
|2nd December||Clustering 2||Raphael Menges|
|9th December||KNN and Naive Bayes||Tjitze Rienstra|
|16th December||Decision Tree||Tjitze Rienstra|
|6th January||Linear Classification||Zeyd Boukhers|
|13th January||Neural Network||Zeyd Boukhers|
|20th January||Deep Learning||Zeyd Boukhers|
|27th January||Hidden Markov Chain||Tjitze Rienstra|
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 and Python Introduction||Raphael Menges|
|19th November||Machine Learning Fundamentals||Raphael Menges|
|26th November||Data Preprocessing||Raphael Menges|
|3rd December||Data Transformation||Raphael Menges|
|10th December||Clustering||Raphael Menges|
|17th December||KNN||Tjitze Rienstra|
|7th January||Naive Bayes||Tjitze Rienstra|
|14th January||Decision Tree||Tjitze Rienstra|
|21st January||SVM||Zeyd Boukhers|
|28th January||Neural Network||Zeyd Boukhers|
|4th February||Deep Learning||Zeyd Boukhers|
|11th February||Hidden Markov Chain||Tjitze Rienstra|
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||Deadline (9:00 AM!)||Topic||Sheets||Remarks|
|9th November||16th November||Machine Learning Fundamentals|
|16th November||23rd November||Data Preprocessing|
|23rd November||30th November||Data Transformation|
|30th November||7th December||Clustering|
|7th December||14th December||KNN|
|14th December||21st December||Naive Bayes|
|4th January||11th January||Decision Tree|
|11th January||18th January||SVM|
|18th January||25th January||Neural Network|
|25th January||1st February||Deep Learning|
|1st February||8th February||Hidden Markov Chain|