Welcome to the Machine Learning and Data Mining course of winter terms 2018/2019!

If you want to participate, (1) register to the lecture and one tutorial in Klips and (2) add yourself into a group for working on the mandatory assignments (information under the assignment headline below). For inter-student communication, please use the newsgroup infko-mldm here.

Schedule

Lecture and Tutorial - Machine Learning and Data Mining (6 ECTS; for Master and Bachelor students in Web Science, Computer Science, Computational Visualistics and Business Informatics). The lecture starts at 8:30 AM. The first tutorial on Thursday starts at 2:15 PM, the second at 4:00 PM. The tutorial on Friday starts at 10:15 AM.

Video Lectures

Lecture recordings are found here.

Course Material

Assignments

Please form groups of three people to work on the assignments here, until 28th 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.
 

Exam

First Exam: 20.02.2019, 8:15, D028. Registrations open on 23.01.2019, registration deadline is on 13.02.2019, deregistration closes on 18.02.2019.
Second Exam: 04.04.2019, 16:00, D028. Registrations open on 11.03.2019, registration deadline is on 27.03.2019, deregistration closes on 29.03.2019.

General remarks about exams:

1. No calculators allowed!
2. Be on time.
3. Total Exam duration: 90 minutes

Information for students from the previous semster about taking the exam of this semester: 
If you had qualified for the exam in the previous semester and participated in an exam that you failed, you may just register for the first exam of this semenster without renewing your qualification.
If you had qualified for the exam and did not participate in an exam in the previous semester, you must renew your qualification to take an exam this semester by participating in an assignment group and gaining a sufficient amout of points.

Literature

Below some pointers to related literature:

• Christopher Bishop. Pattern Recognition and Machine Learning. Free download at: https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf
• An Introduction to Statistical Learning with Applications in R. Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. Free for download: http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Seventh%20Printing.pdf
• The Elements of Statistical Learning: Data Mining, Inference, and Prediction.
  Trevor Hastie, Robert Tibshirani, Jerome Friedman. Free download: https://web.stanford.edu/~hastie/ElemStatLearn/printings/ESLII_print12.pdf
• Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning. MIT Press. Free online at: http://www.deeplearningbook.org/
• A visual introduction to machine learning: http://www.r2d3.us/visual-intro-to-machine-learning-part-1/ 
• http://neuralnetworksanddeeplearning.com/
• Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong. To be published by Cambridge University Press. Mathematics for Machine Learning: https://mml-book.github.io/ https://www.techleer.com/articles/564-a-beginner-mathematics-book-for-machine-learning/