# Machine Learning Course SS 16 U Stuttgart

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See my general teaching page for previous versions of this lecture. The 2015 lists the contents and extra material of the lecture. Here I'll only put the updated slides and exercises online.

The tutorials on Monday 4th are skipped. The first lecture is is April 7th, V38.02, 11:30.

Tutorial rooms
Mo 09:45-11:15 0.124
Mo 11:30-13:00 0.124
Mo 14:00-15:30 0.124
Mo 15:45-17:15 0.108
Example Exam
Here is an example exam: beispielKlausur . Some notes: This was last year's exam, but I removed one question. Generally, an exam will have a number of simple/basic questions, which simply check if you've learnt certain facts/concepts. But also a few questions where you really have to derive/compute something. Topic-wise, the questions are similar to the exercise questions (but hopefully have more well-defined answers...).
Schedule, slides & exercises
 date topics slides exercises(due on following Monday) 7.4. Introduction 01-introduction e01-intro 14.4. Regression 02-regression e02-linearRegression ../data/dataLinReg2D.txt ../data/dataQuadReg2D.txt ../data/dataQuadReg2D_noisy.txt 21.4. Classification & Structured Output 03-classification e03-classification 28.4. e04-classification ../data/data2Class.txt 5.5. holiday e05-structuredOutput ../data/dataMixedCRF.txt 12.5. Kernelization & Structured Input 04-kernelization 05-MLbreadth e06-kernels (due on May 23rd) 2.6. Neural Networks e07-NN (due on June 6th) ../data/data2Class_adjusted.txt 9.6. Clustering e08-PCA 16.6. Boosting e09-clustering ../data/mixture.txt 23.6. Bayesian ML 06-BayesianRegressionClassification e10-boosting-bayes 30.6. COLLECTED SLIDES 16-MachineLearning Open questions & Präzenzübung: e11-gaussianProcesses

Slides of the previous year:

## Was ist Informatik? – Eine andere Sichtweise

Die gängigen Erklärungen zu “Was ist Informatik?” – etwa von der Gesellschaft für Infomatik, der TU Dresden, oder auf Wikipedia – machen es einem schwer, sic...