Uni

Vorlesungszusammenfassung der Vorlesung Rechnerstrukturen am KIT von Prof. Dr. Karl gehalten von Dr. Lars Bauer und Übungen gehalten von Thomas Becker. Die Klausur hat typischerweise einen hohen Anteil an Wissensfragen und die Bearbeitungszeit ist sehr knapp. Toggle all GrundlagenEinführungZunächst mechanische RechnerPlatz und Komplexität durch Dualsystem deutlich reduziertMoore’s Gesetz Anzahl der Transistoren, die auf einem IC integriert werden können, verdoppelt sich alle 18 Monate. Später angepasst auf alle zwei Jahre.

Software Engineering 2 (SWT II) is the follow up lecture to Software Engineering 1 and is held by Prof. Dr. Reussner. It focusses on software architecture, quality and development processes. The first part of this post is a lecture summary organized as self test questions for active recall. Bellow there are answers to the learning goals presented in the last lecture. Design & RealizationClean CodingLehman’s first lawA system that is used will be changed

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. ~ Alan Westin (1967)Privacy Sphere modelModelling protection requirements (expectations) of classes of information as concentric circles of decreasing need for protection.

Abstract Ethereum smart contracts are Turing complete programs that operate on money and derived assets. With a market capitalization in the three digit billions, there is an interest in quantifying their usage. Despite blockchain data being public by design, large scale analysis of smart contracts is technically challenging to do on a large scale. We summarize methods to analyze contract usage on the Ethereum blockchain and categorize the most popular contracts by their application domain and behavior.

The lecture Machine Learning for the Natural Sciences promises to focus on applications of machine learning to natural sciences, especially physics and chemistry. However, most of the actual content is repeating machine learning basics, that is already in foundational lectures on machine learning. In the remaining time, a few interesting are presented, but sadly just very shallowly. There is also programming homework that counts for 1/3 of the final grade. This is nice, and I think more courses should do that.

There is no such thing as the perfect university exam, but if we agree that its purpose is to give an objective score about an individual’s comprehension of the covered topics, then there is clearly a way to be less wrong when creating exams. This post lists a few DONT’S that can be easily avoided. If you think there is an anti-pattern in this post, you can write me an email and I will add it here.

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

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 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 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?