Suchergebnis: Katalogdaten im Herbstsemester 2017

Rechnergestützte Wissenschaften Bachelor Information
Block G2
401-0603-00LStochastikO4 KP2V + 1UM. Schweizer
KurzbeschreibungDie 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.
LernzielKenntnis der Grundlagen der Wahrscheinlichkeitstheorie und Statistik.
InhaltEinführung in die Wahrscheinlichkeitstheorie, einige Grundbegriffe der mathematischen Statistik und Methoden der angewandten Statistik.
252-0834-00LInformation Systems for Engineers Information O4 KP2V + 1UG. Fourny
KurzbeschreibungThis course provides the basics of information systems from the perspective of the user. The main focus is on relational databases, including tabular data, the relational algebra, the SQL query language, schema design, normal forms, physical architecture, indices. The course also covers support for data cubes on top of relational databases.
LernzielAfter visiting this course, students should be capable to:

1. Explain, in the big picture, how a relational database works and what it can do in their 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 a new relational schema to store data in accordance to the real world's constraints, such as relationship cardinality.
5. Adapt and improve an existing schema to make it more robust against anomalies, thanks to a very good theoretical knowledge of normal forms.
6. Understand how indices work (hash indices, B-trees), how they are implemented, and how to use them to make queries faster.
7. Access an existing relational database from a host language such as Java, using bridges such as JDBC.
8. Explain data independence.
9. Explain how a relational database is physically implemented.
10. Know and deal with the natural syntax for relational data, CSV.
11. Explain the data cube model including slicing and dicing.
12. Store data cubes in a relational database.
13. Map cube queries to SQL.
14. Slice and dice cubes in a UI.
InhaltUsing a relational database
1. Introduction (historical overview, data independence, data shapes)
2. The relational model (data models, tables, relational algebra, CSV syntax)
3. The query language SQL (DML 1 of 2, SQL shell, counterpart of selection, projection, grouping, ordering, renaming)
4. Schema definitions (DDL, data types, SQL)
5. Updates (DML 2 of 2, insertion, deletion, updates, SQL)

Taking a relational database to the next level
6. Best practices and normal forms (update/insert/delete anomalies, first, second, third, BC, fourth)
7. Physical architecture of a relational database (storage levels, tuple storage)
8. Indices and optimization (ISAM, B-tree, B+-tree, hash)
9. Communicating with a SQL database from a host language (Java, JDBC)

Analytics on top of a relational database
10. Analytics, data warehousing, OLAP vs. OLTP, the data cube model (dimensions, algebra)
11. Storing and querying data cubes in a relational database (star schema, snowflake schema)
12. Data cube optimization (indices, bitmaps)

13. Limits of tables and cubes (decision points for when and when not to use them vs. trees, graphs)
14. Limits of scaling up a single machine and transition to Big Data (introduction to data denormalization on simple cases, "hacking" a relational database onto several machines and issues)
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 / BesonderesFor 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
401-0647-00LIntroduction to Mathematical Optimization Information O5 KP2V + 1UD. Adjiashvili
KurzbeschreibungIntroduction to basic techniques and problems in mathematical optimization, and their applications to a variety of problems in engineering.
LernzielThe 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.
InhaltTopics 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.
LiteraturInformation about relevant literature will be given in the lecture.
Voraussetzungen / BesonderesThis 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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