Suchergebnis: Katalogdaten im Herbstsemester 2018
Rechnergestützte Wissenschaften Bachelor | ||||||
Bachelor-Studium (Studienreglement 2012 und 2016) | ||||||
Grundlagenfächer | ||||||
Block G2 | ||||||
Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
---|---|---|---|---|---|---|
401-0603-00L | Stochastik | O | 4 KP | 2V + 1U | M. H. Maathuis | |
Kurzbeschreibung | Die Vorlesung deckt folgende Themenbereiche ab: Zufallsvariablen, Wahrscheinlichkeit und Wahrscheinlichkeitsverteilungen, gemeinsame und bedingte Wahrscheinlichkeiten und Verteilungen, das Gesetz der Grossen Zahlen, der zentrale Grenzwertsatz, deskriptive Statistik, schliessende Statistik, Statistik bei normalverteilten Daten, Punktschätzungen, und Vergleich zweier Stichproben. | |||||
Lernziel | Kenntnis der Grundlagen der Wahrscheinlichkeitstheorie und Statistik. | |||||
Inhalt | Einführung in die Wahrscheinlichkeitstheorie, einige Grundbegriffe der mathematischen Statistik und Methoden der angewandten Statistik. | |||||
Skript | Vorlesungsskript | |||||
Literatur | Vorlesungsskript | |||||
252-0834-00L | Information Systems for Engineers | O | 4 KP | 2V + 1U | G. Fourny | |
Kurzbeschreibung | This course provides the basics of relational databases from the perspective of the user. We will discover why tables are so incredibly powerful to express relations, learn the SQL query language, and how to make the most of it. The course also covers support for data cubes (analytics). After this course, you will be ready for Big Data for Engineers. | |||||
Lernziel | After visiting this course, you will be capable to: 1. Explain, in the big picture, how a relational database works and what it can do in your own words. 2. Explain the relational data model (tables, rows, attributes, primary keys, foreign keys), formally and informally, including the relational algebra operators (select, project, rename, all kinds of joins, division, cartesian product, union, intersection, etc). 3. Perform non-trivial reading SQL queries on existing relational databases, as well as insert new data, update and delete existing data. 4. Design new schemas to store data in accordance to the real world's constraints, such as relationship cardinality 5. Explain what bad design is and why it matters. 6. Adapt and improve an existing schema to make it more robust against anomalies, thanks to a very good theoretical knowledge of what is called "normal forms". 7. Understand how indices work (hash indices, B-trees), how they are implemented, and how to use them to make queries faster. 8. Access an existing relational database from a host language such as Java, using bridges such as JDBC. 9. Explain what data independence is all about and didn't age a bit since the 1970s. 10. Explain, in the big picture, how a relational database is physically implemented. 11. Know and deal with the natural syntax for relational data, CSV. 12. Explain the data cube model including slicing and dicing. 13. Store data cubes in a relational database. 14. Map cube queries to SQL. 15. Slice and dice cubes in a UI. And of course, you will think that tables are the most wonderful object in the world. | |||||
Inhalt | Using a relational database ================= 1. Introduction 2. The relational model 3. Data definition with SQL 4. The relational algebra 5. Queries with SQL Taking a relational database to the next level ================= 6. Database design theory 7. Databases and host languages 8. Databases and host languages 9. Indices and optimization 10. Database architecture and storage Analytics on top of a relational database ================= 12. Data cubes Outlook ================= 13. Outlook | |||||
Literatur | - Lecture material (slides). - Book: "Database Systems: The Complete Book", H. Garcia-Molina, J.D. Ullman, J. Widom (It is not required to buy the book, as the library has it) | |||||
Voraussetzungen / Besonderes | For non-CS/DS students only, BSc and MSc Elementary knowledge of set theory and logics Knowledge as well as basic experience with a programming language such as Pascal, C, C++, Java, Haskell, Python | |||||
401-0647-00L | Introduction to Mathematical Optimization | O | 5 KP | 2V + 1U | D. Adjiashvili | |
Kurzbeschreibung | Introduction to basic techniques and problems in mathematical optimization, and their applications to a variety of problems in engineering. | |||||
Lernziel | The goal of the course is to obtain a good understanding of some of the most fundamental mathematical optimization techniques used to solve linear programs and basic combinatorial optimization problems. The students will also practice applying the learned models to problems in engineering. | |||||
Inhalt | Topics covered in this course include: - Linear programming (simplex method, duality theory, shadow prices, ...). - Basic combinatorial optimization problems (spanning trees, shortest paths, network flows, ...). - Modelling with mathematical optimization: applications of mathematical programming in engineering. | |||||
Literatur | Information about relevant literature will be given in the lecture. | |||||
Voraussetzungen / Besonderes | This course is meant for students who did not already attend the course "Mathematical Optimization", which is a more advance lecture covering similar topics. Compared to "Mathematical Optimization", this course has a stronger focus on modeling and applications. |
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