Search result: Catalogue data in Spring Semester 2021

Computer Science Bachelor Information
Basic Courses
NumberTitleTypeECTSHoursLecturers
252-0058-00LFormal Methods and Functional Programming Information O7 credits4V + 2UD. Basin, P. Müller
AbstractIn this course, participants will learn about new ways of specifying, reasoning about, and developing programs and computer systems. The first half will focus on using functional programs to express and reason about computation. The second half presents methods for developing and verifying programs represented as discrete transition systems.
ObjectiveIn this course, participants will learn about new ways of specifying,
reasoning about, and developing programs and computer systems. Our objective is to help students raise their level of abstraction in modeling and implementing systems.
ContentThe first part of the course will focus on designing and reasoning
about functional programs. Functional programs are mathematical
expressions that are evaluated and reasoned about much like ordinary
mathematical functions. As a result, these expressions are simple to
analyze and compose to implement large-scale programs. We will cover the mathematical foundations of functional programming, the lambda calculus, as well as higher-order programming, typing, and proofs of correctness.

The second part of the course will focus on deductive and algorithmic validation of programs modeled as transition systems. As an example of deductive verification, students will learn how to formalize the semantics of imperative programming languages and how to use a formal semantics to prove properties of languages and programs. As an example of algorithmic validation, the course will introduce model checking and apply it to programs and program designs.
252-0063-00LData Modelling and Databases Information O7 credits4V + 2UC. Zhang
AbstractData modelling (Entity Relationship), relational data model, relational design theory (normal forms), SQL, database integrity, transactions and advanced database engines
ObjectiveIntroduction to relational databases and data management. Basics of SQL programming and transaction management.
ContentThe course covers the basic aspects of the design and implementation of databases and information systems. The courses focuses on relational databases as a starting point but will also cover data management issues beyond databases such as: transactional consistency, replication, data warehousing, other data models, as well as SQL.
LiteratureKemper, Eickler: Datenbanksysteme: Eine Einführung. Oldenbourg Verlag, 7. Auflage, 2009.

Garcia-Molina, Ullman, Widom: Database Systems: The Complete Book. Pearson, 2. Auflage, 2008.
252-0064-00LComputer Networks Information O7 credits4V + 2UA. Perrig, A. Singla
AbstractThis introductory course on computer networking takes a top-down view from networked applications all through the physical layer.
ObjectiveStudents will get a comprehensive overview of the key protocols and the architecture of the Internet, as one example of more general principles in network design. Students will also acquire hands-on experience in programming different aspects of a computer networks. Apart from the state-of-the-art in networking practice, students will explore the rationale for the design choices that networks in the past have made, and where applicable, why these choices may no longer be ideal.
Lecture notesThe slides for each lecture will be made available through the course Web page, along with additional reference material.
LiteratureComputer Networking: A Top-Down Approach, James F. Kurose and Keith W. Ross. Pearson; 7th edition (May 6, 2016)
Prerequisites / NoticeThe bonus projects use C programming. ETH courses in the Bachelor track before this course already cover this. For other students, e.g., exchange, please take note of this requirement: you can still take the course and get a good (even 6/6) grade, but if you don't fulfill this prerequisite, you are disadvantaged compared to others who can get the bonus points.
401-0614-00LProbability and Statistics Information Restricted registration - show details O5 credits2V + 2UM. Schweizer
AbstractEinführung in die Wahrscheinlichkeitstheorie und Statistik
Objectivea) Fähigkeit, die behandelten wahrscheinlichkeitstheoretischen Methoden zu verstehen und anzuwenden

b) Probabilistisches Denken und stochastische Modellierung

c) Fähigkeit, einfache statistische Tests selbst durchzuführen und die Resultate zu interpretieren
ContentWahrscheinlichkeitsraum, Wahrscheinlichkeitsmass, Zufallsvariablen, Verteilungen, Dichten, Unabhängigkeit, bedingte Wahrscheinlichkeiten, Erwartungswert, Varianz, Kovarianz, Gesetz der grossen Zahlen, Zentraler Grenzwertsatz, grosse Abweichungen, Chernoff-Schranken, Maximum-Likelihood-Schätzer, Momentenschätzer, Tests, Neyman-Pearson Lemma, Konfidenzintervalle
Lecture notesLernmaterialien sind erhältlich auf Link
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