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.
|31.10.2018||Menges||Optional: Python Tutorial|
|25./26.10.||Tutorial and assignment structure, groups and SVN introduction, first look at assignment 01|
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.
|Release Date||Assignment||Submission Deadline at 9:00AM||Sheets|
|29th October||Pen and Paper: Machine Learning Fundamentals||5th November|
Further details coming soon.
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.
Below some pointers to related literature:
- 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