# Search result: Catalogue data in Autumn Semester 2019

Computer Science (General Courses) | ||||||

Computer Science for Non-Computer Scientists | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |
---|---|---|---|---|---|---|

252-0836-00L | Computer Science II | Z | 4 credits | 2V + 1U | F. Mattern | |

Abstract | Introduction to basic problem solving methods, algorithms, and data structures. Topics: divide and conquer, recursion, sorting algorithms, backtracking, game tree search, data structures (lists, stacks, binary trees, etc.), discrete simulation, concurrency, complexity, verification. In the assignments and exercises, the programming language Java is used. | |||||

Learning objective | Introduction to the general methods of computer science for electrical engineers. Also provides basic skills for advanced exercises and projects later in the electrical engineering program. | |||||

Content | Part II of the lecture concentrates on the most common problem solving skills, algorithms, and data structures. It also teaches fundamental concepts and mechanisms of structured programming. Furthermore, working with formal systems, the necessity of abstraction, and the importance of modeling in computer science will be motivated. The emphasis of the lecture is on practical concepts of computer science. Specific topics are: complexity and correctness of algorithms, divide and conquer, recursion, algorithms for sorting, backtracking, game tree search, data structures (lists, stacks, inary trees, etc.), discrete simulation, concurrency, and verification. For the assignments and exercises, the programming language Java is used. Here, also modularization, abstraction, encapsulation, and object orientation will be considered. Occasionally, short remarks on the historical context of relevant concepts are given. In the practice groups, students program an automatic player for the game "Reversi"; at the end of the semester a tournament will take place. | |||||

Lecture notes | Copies of slides, extended with bonus slides that give hints to advanced concepts and present the historical context of selected concepts. | |||||

Literature | Textbook: Mark Allan Weiss: Data Structures and Problem Solving Using Java, Addison Wesley. | |||||

Prerequisites / Notice | Prerequisite: Part 1 of the course. | |||||

252-0839-00L | Informatics | Z | 2 credits | 2G | L. E. Fässler, M. Dahinden | |

Abstract | Students learn to apply selected concepts and tools from computer science for working on interdisciplinary projects. The following topics are covered: modeling and simulations, visualizing multi-dimensional data, managing data with lists and tables and with relational databases, introduction to programming, universal methods for algorithm design. | |||||

Learning objective | The students learn to - choose and apply appropriate tools from computer science, - process and analyze real-world data from their subject of study, - handle the complexity of real-world data, - know universal methods for algorithm design. | |||||

Content | 1. Modeling and simulations 2. Visualizing multidimensional data 3. Data management with lists and tables 4. Data management with a relational database 5. Introduction to macro programming 6. Introduction to programming with Python | |||||

Lecture notes | All materials for the lecture are available at www.evim.ethz.ch | |||||

Prerequisites / Notice | This course is based on application-oriented learning. The students spend most of their time working through projects with data from natural science and discussing their results with teaching assistants. To learn the computer science basics there are electronic tutorials available. | |||||

252-0845-00L | Computer Science I | Z | 5 credits | 2V + 2U | H. Lehner, F. Friedrich Wicker | |

Abstract | The course covers the basic concepts of computer programming. | |||||

Learning objective | Basic understanding of programming concepts. Students will be able to write and read simple programs and to modify existing programs. | |||||

Content | Variablen, Typen, Kontrollanweisungen, Prozeduren und Funktionen, Scoping, Rekursion, dynamische Programmierung, vektorisierte Programmierung, Effizienz. Als Lernsprache wird Java eingesetzt. | |||||

Literature | Sprechen Sie Java? Hanspeter Mössenböck dpunkt.verlag | |||||

252-0847-00L | Computer Science | Z | 5 credits | 2V + 2U | M. Schwerhoff, F. Friedrich Wicker | |

Abstract | The course covers the fundamental concepts of computer programming with a focus on systematic algorithmic problem solving. Taught language is C++. No programming experience is required. | |||||

Learning objective | Primary educational objective is to learn programming with C++. After having successfully attended the course, students have a good command of the mechanisms to construct a program. They know the fundamental control and data structures and understand how an algorithmic problem is mapped to a computer program. They have an idea of what happens "behind the scenes" when a program is translated and executed. Secondary goals are an algorithmic computational thinking, understanding the possibilities and limits of programming and to impart the way of thinking like a computer scientist. | |||||

Content | The course covers fundamental data types, expressions and statements, (limits of) computer arithmetic, control statements, functions, arrays, structural types and pointers. The part on object orientation deals with classes, inheritance and polymorphism; simple dynamic data types are introduced as examples. In general, the concepts provided in the course are motivated and illustrated with algorithms and applications. | |||||

Lecture notes | English lecture notes will be provided during the semester. The lecture notes and the lecture slides will be made available for download on the course web page. Exercises are solved and submitted online. | |||||

Literature | Bjarne Stroustrup: Einführung in die Programmierung mit C++, Pearson Studium, 2010 Stephen Prata, C++ Primer Plus, Sixth Edition, Addison Wesley, 2012 Andrew Koenig and Barbara E. Moo: Accelerated C++, Addison-Wesley, 2000 | |||||

252-0851-00L | Algorithms and Complexity | Z | 4 credits | 2V + 1U | J. Lengler, A. Steger | |

Abstract | Introduction: RAM machine, data structures; Algorithms: sorting, median, matrix multiplication, shortest paths, minimal spanning trees; Paradigms: divide & conquer, dynamic programming, greedy algorithms; Data Structures: search trees, dictionaries, priority queues; Complexity Theory: P and NP, NP-completeness, Cook's theorem, reductions. | |||||

Learning objective | After this course students know some basic algorithms as well as underlying paradigms. They will be familiar with basic notions of complexity theory and can use them to classify problems. | |||||

Content | Die Vorlesung behandelt den Entwurf und die Analyse von Algorithmen und Datenstrukturen. Die zentralen Themengebiete sind: Sortieralgorithmen, Effiziente Datenstrukturen, Algorithmen für Graphen und Netzwerke, Paradigmen des Algorithmenentwurfs, Klassen P und NP, NP-Vollständigkeit, Approximationsalgorithmen. | |||||

Lecture notes | Ja. Wird zu Beginn des Semesters verteilt. | |||||

252-0852-00L | Foundations of Computer Science | Z | 4 credits | 2V + 2U | L. E. Fässler, M. Dahinden, D. Komm | |

Abstract | Students learn to apply selected concepts and tools from computer science for working on interdisciplinary projects. The following topics are covered: modeling and simulations, introduction to programming, visualizing multi-dimensional data, introduction matrices, managing data with lists and tables and with relational databases, universal methods for algorithm design. | |||||

Learning objective | The students learn to - understand the role of computer science in science, - to control computer and automate processes of problem solving by programming, - choose and apply appropriate tools from computer science, - process and analyze real-world data from their subject of study, - handle the complexity of real-world data. | |||||

Content | 1. The role of computer science in science 2. Introduction to Programming with Python 3. Modeling and simulations 4. Introduction to Matrices with Matlab 5. Visualizing multidimensional data 6. Data management with lists and tables 7. Data management with a relational database | |||||

Lecture notes | All materials for the lecture are available at www.gdi.ethz.ch | |||||

Literature | L. Fässler, M. Dahinden, D. Komm, and D. Sichau: Einführung in die Programmierung mit Python und Matlab. Begleitunterlagen zum Onlinekurs und zur Vorlesung, 2016. ISBN: 978-3741250842. L. Fässler, M. Dahinden, and D. Sichau: Verwaltung und Analyse digitaler Daten in der Wissenschaft. Begleitunterlagen zum Onlinekurs und zur Vorlesung, 2017. | |||||

Prerequisites / Notice | This course is based on application-oriented learning. The students spend most of their time working through projects with data from natural science and discussing their results with teaching assistants. To learn the computer science basics there are electronic tutorials available. | |||||

252-0855-00L | Computer Science in Secondary School Mathematics | Z | 4 credits | 3G | J. Hromkovic, G. Serafini | |

Abstract | The unit "Computer Science in Secondary School Mathematics" addresses key contributions of computer science to general education, the tight relations between the algorithmic and the mathematical way of thinking, and the thoughtful choice of computer science topics for high school mathematics classes. | |||||

Learning objective | The general goal of the course consists in presenting ways to teach fundamentals of computer science, which are closely related to contents and methods of mathematics. After attending the course unit, a mathematics teacher is able to teach selected fundamentals of computer science in mathematics classes. The students understand the fundamental concepts of computer science in the context of a broad and deep knowledge. Through this understanding, they manage to prepare teaching materials for a successful knowledge transfer and to pass their passion for the subject on to their pupils. The students know various teaching methods as well as their advantages and disadvantages. They can handle inhomogeneous prior knowledge of the learners inside a class. Besides holding classes, the students do care about the individual pupil support. They encourage the autonomy of the learners, manage to work with diverse target groups and to establish a positive learning environment. The students are able to express themselves using a comprehensible and refined professional language, both in a spoken and a written way, and they master the basic terminology of computer science. Besides the English terms, they are familiar with the corresponding German expressions. The students are able to produce detailed, matured, linguistically correct and design-wise appealing teaching materials. | |||||

Content | The main topics of the course unit "Computer Science in Secondary School Mathematics" represent a scientific and didactic added value for mathematics classes. The course covers the didactics of logic, of cryptology, of finite state automata, of computability and of the introduction to programming. The students develop the understanding of fundamental scientific concepts such as algorithm, program, complexity, determinism, computation, automata, verification, testing, security of a cryptosystem and secure communication. They reflect on ways to embed them into a scientifically sound and didactically sustainable mathematics course. In a semester exercise, the students develop and document an adaptive teaching unit for computer science. They learn to employ the didactics methods and techniques that are introduced at the beginning of the semester. | |||||

Lecture notes | Literatur wird angegeben. Zusätzliche Unterlagen und Folien werden zur Verfügung gestellt. | |||||

Literature | J. Hromkovic: Sieben Wunder der Informatik: Eine Reise an die Grenze des Machbaren, mit Aufgaben und Lösungen. Vieweg+Teubner; Auflage: 2 (2008). K. Freiermuth, J. Hromkovic, L. Keller und B. Steffen: Einfuehrung in die Kryptologie: Lehrbuch für Unterricht und Selbststudium. Springer Vieweg; Auflage: 2 (2014). J. Hromkovic: Berechenbarkeit: Logik, Argumentation, Rechner und Assembler, Unendlichkeit, Grenzen der Automatisierbarkeit. Vieweg+Teubner; Auflage: 1 (2011). H.-J. Böckenhauer, J. Hromkovic: Formale Sprachen: Endliche Automaten, Grammatiken, lexikalische und syntaktische Analyse. Springer Vieweg; Auflage: 1 (Januar 2013). J. Hromkovic: Einführung in die Programmierung mit LOGO: Lehrbuch für Unterricht und Selbststudium. Springer Vieweg; Auflage: 3 (2014) | |||||

252-0856-00L | Computer Science | Z | 4 credits | 2V + 2U | F. Friedrich Wicker, M. Schwerhoff | |

Abstract | The course covers the fundamental concepts of computer programming with a focus on systematic algorithmic problem solving. Taught language is C++. No programming experience is required. | |||||

Learning objective | Primary educational objective is to learn programming with C++. After having successfully attended the course, students have a good command of the mechanisms to construct a program. They know the fundamental control and data structures and understand how an algorithmic problem is mapped to a computer program. They have an idea of what happens "behind the scenes" when a program is translated and executed. Secondary goals are an algorithmic computational thinking, understanding the possibilities and limits of programming and to impart the way of thinking like a computer scientist. | |||||

Content | The course covers fundamental data types, expressions and statements, (limits of) computer arithmetic, control statements, functions, arrays, structural types and pointers. The part on object orientation deals with classes, inheritance and polymorphism; simple dynamic data types are introduced as examples. In general, the concepts provided in the course are motivated and illustrated with algorithms and applications. | |||||

Lecture notes | English lecture notes will be provided during the semester. The lecture notes and the lecture slides will be made available for download on the course web page. Exercises are solved and submitted online. | |||||

Literature | Bjarne Stroustrup: Einführung in die Programmierung mit C++, Pearson Studium, 2010 Stephen Prata, C++ Primer Plus, Sixth Edition, Addison Wesley, 2012 Andrew Koenig and Barbara E. Moo: Accelerated C++, Addison-Wesley, 2000 | |||||

252-0834-00L | Information Systems for Engineers Does not take place this semester. | Z | 4 credits | 2V + 1U | to be announced | |

Abstract | 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. | |||||

Learning objective | 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. | |||||

Content | 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 | |||||

Literature | - 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) | |||||

Prerequisites / Notice | 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 |

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