Machine Learning and Data Mining[go to overview]
Winter Terms 2019 / 2020
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 optimization and deep learning. Excited to participate? Then, you need to:
- Register to the lecture in Klips
- Register to one tutorial in Klips
- Add yourself into a group for working on mandatory assignments here until 25th October
When you are newly enrolled and have not yet access to the infrastructure of our University, you may still take part in the course. You must perform the following steps until 25th of October:
- Come up with groups of four students. If your group has less than four students, we will merge you with other students without further notice.
- Send one (!) email per group (!) to email@example.com with the title “MLDM GROUP REGISTRATION” and the full names (as provided at enrollment) + current email address of each team member.
- We will tell you your group name for submission and the further process of submission then.
For inter-student communication, please use the newsgroup infko-mldm.
Master and Bachelor students in:
- Web Science
- Computer Science
- Computational Visualistics
- Business Informatics
- When: February 12 at 08:30 (Arrive no later than 15 minutes before the start time!)
- Where: M 001
- Duration: 90 minutes
- Registration (Klips): Open from December 30 to February 5 (Do Not miss the deadline!)
- Cancellation (Klips): Until February 7
- You must have gathered at least 60% of the points across the assignments of this semester or have failed the exam in the last year to be allowed to participate in the exam. Admissions from recent years are not valid.
- Lecturer: Dr. Zeyd Boukhers
- Wednesdays, 08:00 - 10:00 in M 001
- Consultation: Mondays at 09:00 in B114 or by appointment (firstname.lastname@example.org)
- Instructors: Akram Sadat Hosseini, Raphael Menges and Qusai Ramadan
- Thursdays, 14:00 - 16:00, M 001
- Thursdays, 16:00 - 18:00, M 001
- You don't need to come to both slots for the tutorial. We will cover the same material on both.
Please form groups of four people to work on the assignments here, until 25th of October! 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 10 points, you need in total at minimum 60 points in sum over all assignments to participate in the exam.
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.
|23rd Oct||Introduction||Zeyd Boukhers||PDF (Uni- account) or PDF (With passwd)||Video|
|30th Oct||Data Preprocessing||Zeyd Boukhers||PDF (Uni- account) or PDF (With passwd)||Video|
|6th Nov||Classification Part 1||Zeyd Boukhers||PDF (Uni- account) or PDF (With passwd)||Video|
|13th Nov||Classification Part 2||Zeyd Boukhers||Video|
|20th Nov||Decision Tree||Zeyd Boukhers||Video|
|27th Nov||Random Forest||Zeyd Boukhers||Video|
|4th Dec||Linear Classification||Zeyd Boukhers||Video|
|11th Dec||Neural Networks||Zeyd Boukhers||Video|
|18th Dec||Test Exam|
|8th Jan||Deep Neural Networks||Zeyd Boukhers||Video|
|15th Jan||Context-dependent Classification||Zeyd Boukhers||Video|
|22th Jan||Data Transformation||Zeyd Boukhers|
|29th Jan||Clustering Part 1||Zeyd Boukhers|
|5th Feb||Clustering Part 2||Zeyd Boukhers|
Tutorials will discuss the solutions to the last assignment and discuss the exercises of the current one. Solutions will not be uploaded!
|31st Oct||Organizational Notes||Akram Sadat Hosseini, Qusai Ramadan, and Raphael Menges||Notes|
|7th Nov||Machine Learning Fundamentals||Qusai Ramadan|
|14th Nov||Data Preprocessing and Visualization||Raphael Menges|
|21st Nov||k-Nearest Neighbors||Akram Sadat Hosseini|
|28th Nov||Naive Bayes||Raphael Menges|
|5th Dec||Decision Tree||Akram Sadat Hosseini|
|12th Dec||Decision Tree (Programming)||Akram Sadat Hosseini|
|19th Dec||SVM||Qusai Ramadan|
|9th Jan||Test Exam||Akram Sadat Hosseini||testexam.pdf|
|16th Jan||Neural Network||Qusai Ramadan|
|23rd Jan||Neural Network (Programming)||Raphael Menges|
|30th Jan||Hidden Markov Chain||Raphael Menges|
|6th Feb||Clustering||Qusai Ramadan|
Assignments will be provided along with the progress of the tutorials.
|Release Date||Deadline (9:00 AM!)||Topic||Sheets||Remarks|
|28th Oct||4th Nov||Pen and Paper: Machine Learning Fundamentals||assignment01.pdf||Alternative link, protected with same password as lectures. Task 1b) has been updated on 28th October, 5 p.m.|
|4th Nov||11th Nov||Programming: Data Preprocessing and Visualization||assignment02.pdf assignment02.csv||Adapted the task descriptions to the notation of the lecture. PDF has been updated on 6th November, 3 p.m.|
|11th Nov||18th Nov||Pen and Paper / Programming: k-Nearest-Neighbors||assignment03.pdf assignment03.zip||Updated on 10th November, 9 p.m. Additional info: You may also import and use the module "math"|
|18th Nov||25th Nov||Pen and Paper: Naive Bayes||assignment04.pdf|
|25th Nov||2nd Dec||Pen and Paper: Decision Tree||assignment05.pdf|
|2nd Dec||9th Dec||Programming: Decision Tree||assignment06.pdf assignment06.ipynb|
|9th Dec||16th Dec||Pen and Paper: SVM||assignment07.pdf|
|6th Jan||13th Jan||Pen and Paper: Neural Network||assignment08.pdf|
|13th Jan||20th Jan||Programming: Neural Network||assignment09.pdf assignment09.zip (.ipynb inside)|
|20th Jan||27th Jan||Pen and Paper: Hidden Markov Chain|
|27th Jan||3rd Feb||Pen and Paper: Clustering|