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 09:4511:15 0.124
Mo 11:3013:00 0.124
Mo 14:0015:30 0.124
Mo 15:4517:15 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. Topicwise, the questions are similar to the exercise questions (but hopefully have more welldefined answers...).
 Schedule, slides & exercises

date topics slides exercises
(due on following Monday)7.4. Introduction 01introduction e01intro 14.4. Regression 02regression e02linearRegression
../data/dataLinReg2D.txt
../data/dataQuadReg2D.txt
../data/dataQuadReg2D_noisy.txt21.4. Classification & Structured Output 03classification e03classification 28.4. e04classification
../data/data2Class.txt5.5. holiday e05structuredOutput
../data/dataMixedCRF.txt12.5. Kernelization & Structured Input 04kernelization
05MLbreadthe06kernels (due on May 23rd) 2.6. Neural Networks e07NN (due on June 6th)
../data/data2Class_adjusted.txt9.6. Clustering e08PCA 16.6. Boosting e09clustering
../data/mixture.txt23.6. Bayesian ML 06BayesianRegressionClassification e10boostingbayes 30.6. COLLECTED SLIDES 16MachineLearning Open questions & Präzenzübung: e11gaussianProcesses
Slides of the previous year:
 ../LectureMachineLearning
 ../15MachineLearning/01introduction
 ../15MachineLearning/02regression
 ../15MachineLearning/03classification
 ../15MachineLearning/04MLbreadth
 ../15MachineLearning/05probabilities
 ../15MachineLearning/06BayesianRegressionClassification
 ../15MachineLearning/07graphicalModels
 ../15MachineLearning/08graphicalModelsLearning