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In practice, one sets up a fairly general model (such as a neural network or a kernelized support vector machine) and often does as little modeling of the task at hand as possible.

I had a large “ask anything” meeting, I had students coming to office hours, and I had “digital office hours” where the students could ask question via a messenger in a chat room.

They were studying heavy on these exercises, barely using the textbook or their lecture notes to look up theorems or definitions (in other words, some were working “model free” or with a “general purpose model” which says something like “do computations following general rules”).

Also some known problems with machine learning methods can be observed with the students: Students get stuck in local minima (they reach a point where further improvement in impossible by revising the seen data –

Students overfit to the training data (on the test data, aka the exam, they face new problems and oftentimes apply the learned methods to tasks where they don’t work, getting wrong results which would be true if the problem would be a little different).

when the students have more techniques at hand, which is related to a more flexible machine learning method, they do better on unseen data and do not get stuck at bad local minima where no improvement is possible.

Exam 70-774

We recommend that you review this exam preparation guide in its entirety and familiarize yourself with the resources on this website before you schedule your exam.

See the Microsoft Certification exam overview for information about registration, videos of typical exam question formats, and other preparation resources.

To help you prepare for this exam, Microsoft recommends that you have hands-on experience with the product and that you use the specified training resources.

Machine learning with kernel methods, Spring 2018

Many problems in real-world applications of machine learning can be formalized as classical statistical problems, e.g., pattern recognition, regression or dimension reduction, with the caveat that the data are often not vectors of numbers.

For example, protein sequences and structures in computational biology, text and XML documents in web mining, segmented pictures in image processing, or time series in speech recognition and finance, have particular structures which contain relevant information for the statistical problem but can hardly be encoded into finite-dimensional vector representations.

We will start with a presentation of the theory of positive definite kernels and reproducing kernel Hilbert spaces, which will allow us to introduce several kernel methods including kernel principal component analysis and support vector machines.

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