DATA AND KNOWLEDGE

ENGINEERING GROUP

This web page gives information on the lecture 'Machine Learning' which is held during winter term 2022/2023 by Andreas Nürnberger. It will be constantly updated during the course. The rest of the course information will be provided via Moodle! (link to be given)

The course provides an introduction to the principles, techniques, and applications of Machine Learning. Topics covered include among others (subject to change):

• value functions
• concept spaces and concept learning
• instance based learning
• clustering
• decision trees
• neural networks
• Bayesian learning
• reinforcement learning
• association rule learning
• genetic algorithms

This course does not offer the possibility to acquire an extra credit point.

Course Schedule and Room Assignments

All dates and times were changed, please check below!

Important: Current information is only found on the Moodle page.

Registration

Registration for the individual groups is done via LSF! Registrations are open!!

Warning: If you do not register for any exercise via the LSF, you will not be allowed to write the exam.

Course Staff

If you have any questions concerning the lectures or assignments please contact (if possible by email):

• Lectures: Andreas Nürnberger
  E-Mail: andreas.nuernberger@ovgu.de
• Exercises and Exam: Marcus Thiel
  E-Mail: marcus.thiel@ovgu.de
• Tutors:
  • Behnam Ensan
  • Sayed Abid Hashimi

Requirements for the Written Exam and the 'Schein'

All students are required to participate in the exercise classes. Every week, there will be an assignment sheet that will be handed out one week in advance. This sheet has to be prepared by every student, uploaded to Moodle and will be discussed in class. There are two different types of assignments: questions of understanding and programming assignments. The programming assignments can be solved in small groups of up to three students and must be sent in before the respective deadline. Prerequisites for a written exam and a 'Schein' is fulfillment of the following criteria:

• Gaining at least 1/2 of all programming points
• Solving at least 2/3 of all questions of understanding
• Presenting at least 1 solution in class.

The exam will be written. For the 'Schein', you have to write the exam as well. The time and date of the exam will be announced later in the course of the semester.

A general reminder: In accordance with the examination rules, we offer each student exactly one examination date (oral or written) each term. The registration for a follow-up examination is only possible in the next term (i.e. after 6 months). As soon as a student has registered for an exam, either by using the LSF for written exams or by filling in the information on an examination list for oral exams (or filling out a registration form), this is counted as the agreed examination date. If it is cancelled, the rule above applies.

General remarks concerning the exam:

• The main focus will be on the topics, that were also discussed in the exercises.
• Theoretical questions (knowledge and understanding) will be from all parts of the lecture.
• Practical tasks will be similar to the exercise assignments.

Materials

Materials will be given in the Moodle.

Other Resources

• Exam Example Questions

Literature

• Machine Learning
  Tom Mitchell
  McGraw-Hill, 1997.
• Artificial Intelligence: A Modern Approach
  S. Russel and P. Norvig
  Prentice Hall, Englewood Cliffs, 2003
• Introduction to Artificial Intelligence (German version: Grundkurs Künstliche Intelligenz)
  Wolfgang Ertel
  Springer-Verlag London, 2011 (German version: Springer Vieweg, 2013)
• Pattern Recognition and Machine Learning
  Christopher M. Bishop
  Springer-Verlag New-York, 2006

Frequently Asked Questions 

• Question: Do I have to register for the course? If so, where?
  Answer: Yes, you have to register for one of the exercises in the LSF! Please make sure to register for the exercise, that you would like to attend, since the capacity (max. 16 per exercise date due to Corona!) is limited!
• Question: I can not be there for the first exercise! What should I do?
  Answer: Missing the first exercise usually is not that big of a problem. You should inform me (Marcus Thiel) about this circumstance though, since I will give unused slots to people on the waiting list otherwise.
• Question: Do I really have to do the first assignment for the FIRST exercise?
  Answer: Yes, of course. The questions are easy enough to be handled without having heard the lecture.
• Question: What is that thing about "voting"?
  Answer: It means, that you have to prepare the assignments BEFORE the exercise, in which they are discussed in. You have to upload your solutions for the specific tasks on Moddle. Generally, we do not check all solutions due to time reasons, but you have to be ready to present your solution in class if being asked for! Failing to do that results in 0 points for all the assignments in the specific exercise.
• Question: How many "votings" do I need in order to get my exam admission?
  Answer: Please try to solve 2/3 of the tasks. Every task on each assignment sheet basically counts as a single point. I also expect active involvement in the class. If you have shown active involvement, but could NOT solve at least 2/3 of the tasks, we can talk about making an exception.
• Question: I can not solve 2/3 of the tasks, since I can not be there for all exercises. What should I do?
  Answer: At least try to upload enough assignments to Moodle to fulfill the 2/3 criteria. If after that it still not is enough due to missing attendance or not received points, there might be possibilities to work something out.
• Question: Is this all I need for my admission?
  Answer: No! There also the programming assigments, that you have to solve during the semester. Exact information on that will be given at the beginning of the semester.
• Question: What exactly are these programming assignments?
  Answer: They will focus on the application and understanding of the different methods, that you will learn in the course. A basic understanding of programming is therefore required.
• Question: But I'm not from a Computer Science background / I can't program! Can I do something else instead?
  Answer: No, sorry. Now would be the best time to learn a bit about programming. You will need that anyways.
• Question: I have never heard anything about Data Mining and/or Machine Learning before. Should I visit this course?
  Answer: That is totally fine. This course is intended to be a basic course for Machine Learning. You only need some basic knowledge about math (e.g. vectors, matrices, derivations).
• Question: My math skills are very rusty, do I need them for this course?
  Answer: You need to have basic skills (e.g. derivations, matrices, vectors) in order to understand this course. Those things will only partially be explained again, so you should be ready to fill the gaps yourself.
• Question: I really, really like Machine Learning, but the assignments are way too difficult and time consuming! Could we please reduce the amount of work?
  Answer: At the moment: No. The assignments may change depending on the feedback though.
• Question: How will the exam look like? Will it be oral or written?
  Answer: The exam will be written. It will be offered at the end of the semester, probably in mid-February. A follow-up exam will then be again in summer, some time in July or beginning of August.