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.
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.
Lecture recordings are found here.
|31.10.2018||Menges||Optional: Python Tutorial||PDF, sampledata.csv, working CheatSheetLink|
|21.11.2018||Staab||Decision trees||PPTX, PDF|
|05.12.2018||Staab||Support Vector Machines||PPTX, PDF|
|12.12.2018||Staab||Feed Forward Networks||PPTX, PDF|
|19.12.2018||Menges / Ramadan||Optional: Test Exam|
|09.01.2019||Staab||Feed forward networks 2||PPTX, PDF|
|16.01.2019||Staab||Feed forward networks 3||PPTX, PDF|
|30.01.2019||Staab||Clustering Part 1|
|06.02.2019||Staab||Clustering Part 2||PPTX, PDF|
|25./26.10.||Tutorial and assignment structure, groups and SVN introduction, first look at assignment 01||tutorial01.pdf||Qusai & Raphael|
|08./09.11.||Discussion of assignment 01, preview of assignment 02||Blackboard||Qusai|
|15./16.11.||Discussion of assignment 02, preview of assignment 03||Blackboard||Raphael|
|22./23.11.||Discussion of assignment 03, preview of assignment 04||Blackboard||Qusai|
|29./30.11.||Discussion of assignment 04, preview of assignment 05||Hint for Assignment 05||Raphael|
|06./07.12.||Discussion of assignment 05, preview of assignment 06||Blackboard||Qusai|
|13./14.12.||Discussion of assignment 06, preview of assignment 07||Blackboard||Raphael|
|20./21.12||Discussion of assignment 07||Blackboard||Qusai|
|10./11.01.||Discussion of test exam, preview of assignment 08||Blackboard||Raphael|
|17./18.01.||Discussion of assignment 08, preview of assignment 09||Blackboard||Qusai|
|24./25.01.||Discussion of assignment 09, preview of assignment 10||Blackboard||Qusai|
|31.01./01.02.||Discussion of assignment 10, preview of assignment 11||Blackboard||Raphael|
|07./08.02.||Discussion of assignment 11||Blackboard||Raphael|
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|
|24th October||Pen and Paper: Machine Learning Fundamentals||5th November||assignment01.pdf|
|5th November||Programming: Machine Learning Fundamentals||12th November||assignment02.pdf
|10th November||Pen and Paper / Programming: k-Nearest Neighbors||19th November||assignment03.pdf
Remarks: For both tasks, use kNN with default rule. In task 2, use micro-average precision.
|17th November||Pen and Paper: Naive Bayes||26th November||assignment04.pdf|
|25th November||Programming: Naive Bayes||3rd December||assignment05.pdf
Update of PDF (sharpening terminology and fix of pointers to slides)
|1st December||Pen and Paper: Decision Tree||10th December||assignment06.pdf|
|8th December||Programming: Decision Tree||17th December||assignment07.pdf
No submission via SVN, not counting into assignment nor final exam grading.
|4th January||Pen and Paper: SVM||14th January||assignment08.pdf
Remarks: 1.1: SVN classifier -> SVM classifier. 2: b = -1.
|12th January||Pen and Paper: Neural Network||21st January||assignment09.pdf|
|18th January||Programming: Neural Network||28th January||assignment10.pdf
|15th January||Pen and Paper: Clustering||4th February||assignment11.pdf|
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:
- No calculators allowed!
- Be on time.
- 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.
Below some pointers to related literature:
- Data Science Cheat Sheet
- 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/
- 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/