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Privacy Enhancing Technologies Summary

This is a summary of the lecture by Prof. Dr. Thorsten Strufe at KIT. The summary is organized as toggles, meant to help active review. PrivacyDefinitionsPrivacy dictionary definitionthe quality or state of being apart from company or observation : seclusion freedom from unauthorized intrusion <one's right to privacy>⇒ right to be let alone CS definition of privacythe claim of individuals … to determine for themselves when, how, and what extent of information about them is communicated to others.


Operation System Security Lecture Summary

Lecture summary of the lecture operation systems security, organized with self test toggles. The lecture is concerned with binary exploitation from an offensive as well as a defensive point of view. I can really recommend the lecture, if you are interested in modern security mechanisms implemented by operating systems and hardware. Basic DefinitionsWhat is a vulnerability?What is the definition of an exploit? Set-uid-bitAllows an executable, that is owned by the user, to use root privileges during execution

Self Test Questions Data Science I

Answers to self test questions for the lecture “Data Science I” at KIT. If you spot any errors, write me an e-mail or Discord message. Lecture 1: IntroductionGive examples of applications of clustering.Customer groups clustered based on bought productsUnsupervised malware family identificationOutlier DetectionDescribe a scenario from natural sciences, in which classification is useful: What are the attributes/class? How would you try to solve it?Flower family classification: Attributes (features)Color of different partsShape of different partsSize of different partsSolve it by training a multi-class NN with enough high quality training dataExplain the principle of the One Rule classifier.

Self Test Questions Machine Learning

Self test questions for the lecture “Machine Learning - Foundations and Algorithms” at KIT. Lecture 3: Model SelectionWhy is it a bad idea to evaluate your algorithm on the training set?Evaluating on the training set, rewards overfitting. Overfitting means learning training points by heart, instead of approximating the distribution the training points were drawn from. A trivial algorithm that just stores and queries all training points, has 100 % accuracy on the training set.


Self Test Questions Entrepreneurship

Self test questions for the lecture entrepreneurship at KIT. Session 1: IntroductionWhat is the activity of an entrepreneur according to Jean Baptiste Say?The entrepreneur shifts economic resources out of an area of lower and into an area of higher productivity. 🐎How does Joseph Schumpeter define “entrepreneurship”?Entrepreneurship is about new factor combinations leading to new products, production methods or new markets. It is about creative destruction. 🤯What are the career reasons of nascent entrepreneurs?

Wiederholungsfragen für Vertragsgestaltung am KIT

Antworten auf die Wiederholungsfragen der Vorlesung Vertragsgestaltung am KIT, die von Rechtsanwalt Stephan Leipert als Gastdozent gehalten wird. Schaut auch auf seinem YouTube-Kanal vorbei. Die Antworten stammen von mir und können Fehler enthalten. Falls solche auffallen sollten oder es andere Ergänzungen geben sollte, schreibt mir eine Mail an die Adresse im About. Die Antworten erscheinen mit einem Klick auf die Frage. Markus Bilz hat ebenfalls die Fragen auf seiner Webseite beantwortet.

Telematics at KIT

In this post I give a visual overview over the lecture “Telematik” at KIT and share my answers to the comprehension questions and the poll questions. Topics of the lecture # I use mindmaps like this to track learning process over all topics. (if you are visually impaired: text form) Comprehension questions # You can compare your answers to mine, by expanding the toggles. Disclaimer: I wrote most of this on a train ride with fogged glases, so if you spot any mistakes or have anything to add, drop me a mail at wachter[punkt]blog[ätt]habmalnefrage[punkt]de


Machine Learning for Computer Security at KIT

Overview # Topics from the lecture. I use mind maps like this before exam preparation to look see what I can remember from during the semester. During learning, I expand on the topics. Later I use color coding of the nodes to visualize what topics I know well and what still need practice. I also made an Anki deck. But sadly the deck contains screenshots of slides that are not licenced under a free licence.