Search result: Catalogue data in Spring Semester 2024
Computer Science TC  Detailed information on the programme at: www.didaktischeausbildung.ethz.ch | |||||||||||||||||||||||||||||||||||||||
 Educational ScienceGeneral course offerings in the category Educational Science are listed under "Programme: Educational Science for Teaching Diploma and TC". | |||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | ||||||||||||||||||||||||||||||||||
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| 851-0240-01L | Designing Learning Environments for School (EW2 TD)  Prerequisites: successful participation in 851-0240-00L "Human Learning (EW1)". Adresses to students enrolled either in Teaching Diploma* (TD) or Teaching Certificate (TC) in Computer Science, Mathematics or Physics. *Except for students of Sport Teaching Diploma, who complete the sport-specific course unit EW2. | O | 3 credits | 2V | E. Stern, M. Rau | ||||||||||||||||||||||||||||||||||
| Abstract | Teaching is a complex skill. The lecture comprises (a) presentations about the theoretical background of this skill, (b) discussions of practical aspects, and (c) practical exercises. | ||||||||||||||||||||||||||||||||||||||
| Learning objective | The participants have the conceptual und procedural knowledge, and skills necessary for long-term planning, preparing, and implementing good lessons. They can apply this knowledge on different topics of their scientific STEM-background. | ||||||||||||||||||||||||||||||||||||||
| Content | We discuss characteristics of successful lessons and how to design such lessons by using curricula and lesson plans, teaching goals and a variety of teaching methods. | ||||||||||||||||||||||||||||||||||||||
| Lecture notes | The lecture comprises interactive parts where the participants elaborate and extend their knowledge and skills. Thus, there is no comprehensive written documentation of the lecture. The participants can download presentation slides, learning materials, and templates from "Moodle". | ||||||||||||||||||||||||||||||||||||||
| Literature | The necessary literature can be downloaded from "Moodle". | ||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | The lecture EW2 can only be attended by students who already successfully completed the lecture Human Learning (EW1). There will be two independent lectures for different groups of students. You will get further information in an email at the beginning of the semester. To get the Credits you have to - regularly attend to the lecture - have the grade 4 or higher in the final written exam. | ||||||||||||||||||||||||||||||||||||||
| 851-0240-24L | Designing Learning Environments for Schools (EW2 LD) - Portfolio - Enrolment only possible with simultaneous enrolment in course 851-0240-01L Designing Learning Environments for School (EW2 LD)! - Prerequisites: successful participation in 851-0240-00L "Human Learning (EW1)". - Adresses to students enrolled either in Teaching Diploma* (TD) or Teaching Certificate (TC) in Computer Science, Mathematics or Physics. *Except for students of Sport Teaching Diploma, who complete the sport-specific course unit EW2. | O | 1 credit | 2U | E. Stern, M. Rau | ||||||||||||||||||||||||||||||||||
| Abstract | In this lecture, you design a portfolio, i.e. a complete and elaborated teaching enviroment for schools, based on your scientific STEM-background | ||||||||||||||||||||||||||||||||||||||
| Learning objective | This lecture is an implementation and transfer of the theoretical inputs provided by the lecture "Designing Learning Environments for School" (EW2). | ||||||||||||||||||||||||||||||||||||||
| 851-0242-06L | Cognitively Activating Instructions in MINT Subjects Enrolment only possible with matriculation in Teaching Diploma or Teaching Certificate (excluding Teaching Diploma Sport). This course unit can only be enroled after successful participation in, or during enrolment in the course 851-0240-00L "Human Learning (EW 1)". | W | 2 credits | 2S | R. Schumacher | ||||||||||||||||||||||||||||||||||
| Abstract | This seminar focuses on teaching units in chemistry, physics and mathematics that have been developed at the MINT Learning Center of the ETH Zurich. In the first meeting, the mission of the MINT Learning Center will be communicated. Furthermore, in groups of two, the students will intensively work on, refine and optimize a teaching unit following a goal set in advance. | ||||||||||||||||||||||||||||||||||||||
| Learning objective | - Get to know cognitively activating instructions in MINT subjects - Get information about recent literature on learning and instruction | ||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Für eine reibungslose Semesterplanung wird um frühe Anmeldung und persönliches Erscheinen zum ersten Lehrveranstaltungstermin ersucht. | ||||||||||||||||||||||||||||||||||||||
| 851-0242-07L | Human Intelligence Enrolment only possible with matriculation in Teaching Diploma or Teaching Certificate (excluding Teaching Diploma Sport). This course unit can only be enroled after successful participation in, or imultaneous enrolment in the course 851-0240-00L "Human Learning (EW 1)" . | W | 1 credit | 1S | E. Stern | ||||||||||||||||||||||||||||||||||
| Abstract | The focus will be on the book "Intelligenz: Grosse Unterschiede und ihre Folgen" by Stern and Neubauer. Participation at the first meeting is obligatory. It is required that all participants read the complete book. Furthermore, in two meetings of 90 minutes, concept papers developed in small groups (5 - 10 students) will be discussed. | ||||||||||||||||||||||||||||||||||||||
| Learning objective | - Understanding of research methods used in the empirical human sciences - Getting to know intelligence tests - Understanding findings relevant for education | ||||||||||||||||||||||||||||||||||||||
| 851-0242-08L | Research Methods in Educational Science Does not take place this semester. Does not take place this semester. This course unit can only be enroled after successful participation in, or imultaneous enrolment in the course 851-0240-00L "Human Learning (EW 1)" . | W | 1 credit | 2S | |||||||||||||||||||||||||||||||||||
| Abstract | Literature from learning sciences will be read and discussed. Research methods will be in focus. At the first meeting all participants will be allocated to working groups and two further meetings will be set up with the groups. In the small groups students will write critical short essays about the read literature. The essays will be presented and discussed in the plenum at the third meeting. | ||||||||||||||||||||||||||||||||||||||
| Learning objective | - Understand research methods used in the empirical educational sciences - Understand and critically examine information from scientific journals and media - Understand pedagogically relevant findings from the empirical educational sciences | ||||||||||||||||||||||||||||||||||||||
| 851-0228-00L | MINTerlink: Formation of Knowledge in STEM Fields in Primary and Secondary School Adresses to students enrolled either in Teaching Diploma* (TD) or Teaching Certificate (TC) (excluding Teaching Diploma Sport). This course unit can only be enroled after successful participation in the course 851-0240-00L "Human Learning (EW 1)". | W | 2 credits | 1S | U. Markwalder | ||||||||||||||||||||||||||||||||||
| Abstract | The event includes a block seminar as well as an assistance period in a primary or secondary school . It is part of a project with the goal of an exchange of expertise: ETH LD students assist primary and secondary school teachers in STEM lessons. | ||||||||||||||||||||||||||||||||||||||
| Learning objective | Deepening the understanding of knowledge formation and learning processes of primary and secondary students from a cognitive and developmental psychology perspective for LD students. The assistantship provides didactic experience and exposure to a different school level (more heterogeneous groups such as for example low-performing to very high-performing Children, language problems etc.) | ||||||||||||||||||||||||||||||||||||||
| Content | LD students learn more about potentials and deficits of students. They get to know better the early stages of knowledge as well as the formation of misconceptions of students in their subject area. The seminar with assistantship includes three phases: In the block seminar misconceptions in the own subject as well as theoretical inputs from developmental and cognitive psychology are discussed (takes place partially in English). During the assistantship, a teaching task defined by the primary and secondary teachers is actively taken on in a class. At the end there is the writing of a final report, which includes the description of the knowledge level of the students. This seminar is only suitable for LD students who can flexibly adapt to the needs of students from lower grades. | ||||||||||||||||||||||||||||||||||||||
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| Subject Didactics and Professional Training Important: You can only enrole in the courses of this category if you have not more than 12 CP left for possible additional requirements. | |||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | ||||||||||||||||||||||||||||||||||
| 271-0102-00L | Teaching Internship Including Examination Lessons in Computer Science Teaching Internship Computer Science for TC. Repetition of the Teaching Internship is excluded even if Examination Lessons are to be repeated. | O | 4 credits | 9P | D. Komm, G. Serafini | ||||||||||||||||||||||||||||||||||
| Abstract | Students apply the insights, abilities and skills they have acquired within the context of an educational institution. They observe 10 lessons and teach 20 lessons independently. Two of them are as assessed as Examination Lessons. | ||||||||||||||||||||||||||||||||||||||
| Learning objective | - Students use their specialist-subject, educational-science and subject-didactics training to draw up concepts for teaching. - They are able to assess the significance of tuition topics for their subject from different angles (including interdisciplinary angles) and impart these to their pupils. - They learn the skills of the teaching trade. - They practise finding the balance between instruction and openness so that pupils can and, indeed, must make their own cognitive contribution. - They learn to assess pupils' work. - Together with the teacher in charge of their teacher training, the students constantly evaluate their own performance. | ||||||||||||||||||||||||||||||||||||||
| Content | Die Studierenden sammeln Erfahrungen in der Unterrichtsführung, der Auseinandersetzung mit Lernenden, der Klassenbetreuung und der Leistungsbeurteilung. Zu Beginn des Praktikums plant die Praktikumslehrperson gemeinsam mit dem/der Studierenden das Praktikum und die Arbeitsaufträge. Die schriftlich dokumentierten Ergebnisse der Arbeitsaufträge sind Bestandteil des Portfolios der Studierenden. Anlässlich der Hospitationen erläutert die Praktikumslehrperson ihre fachlichen, fachdidaktischen und pädagogischen Überlegungen, auf deren Basis sie den Unterricht geplant hat und tauscht sich mit dem/der Studierenden aus. Die von dem/der Studierenden gehaltenen Lektionen werden vor- und nachbesprochen. Die Themen für die beiden Prüfungslektionen am Schluss des Praktikums erfahren die Studierenden in der Regel eine Woche vor dem Prüfungstermin. Sie erstellen eine Vorbereitung gemäss Anleitung und reichen sie bis am Vortrag um 12 Uhr den beiden Prüfungsexperten (Fachdidaktiker/-in, Departementsvertreter/-in) ein. Die gehaltenen Lektionen werden kriteriumsbasiert beurteilt. Die Beurteilung umfasst auch die schriftliche Vorbereitung und eine mündliche Reflexion des Kandidaten/der Kandidatin über die gehaltenen Lektionen im Rahmen eines kurzen Kolloquiums. | ||||||||||||||||||||||||||||||||||||||
| Lecture notes | Dokument: schriftliche Vorbereitung für Prüfungslektionen. | ||||||||||||||||||||||||||||||||||||||
| Literature | Wird von der Praktikumslehrperson bestimmt. | ||||||||||||||||||||||||||||||||||||||
| 272-0103-00L | Mentored Work Subject Didactics Computer Science A Mentored Work Subject Didactics in Computer Science for TC andTeaching Diploma. | O | 2 credits | 4A | D. Komm, J. Hromkovic, G. Serafini | ||||||||||||||||||||||||||||||||||
| Abstract | In their mentored work on subject didactics, students put into practice the contents of the subject-didactics lectures and go into these in greater depth. Under supervision, they compile tuition materials that are conducive to learning and/or analyse and reflect on certain topics from a subject-based and pedagogical angle. | ||||||||||||||||||||||||||||||||||||||
| Learning objective | The objective is for the students: - to be able to familiarise themselves with a tuition topic by consulting different sources, acquiring materials and reflecting on the relevance of the topic and the access they have selected to this topic from a specialist, subject-didactics and pedagogical angle and potentially from a social angle too. - to show that they can independently compile a tuition sequence that is conducive to learning and develop this to the point where it is ready for use. | ||||||||||||||||||||||||||||||||||||||
| Content | Thematische Schwerpunkte Die Gegenstände der mentorierten Arbeit in Fachdidaktik stammen in der Regel aus dem gymnasialen Unterricht. Lernformen Alle Studierenden erhalten ein individuelles Thema und erstellen dazu eine eigenständige Arbeit. Sie werden dabei von ihrer Betreuungsperson begleitet. Gegebenenfalls stellen sie ihre Arbeit oder Aspekte daraus in einem Kurzvortrag vor. Die mentorierte Arbeit ist Teil des Portfolios der Studierenden. | ||||||||||||||||||||||||||||||||||||||
| Lecture notes | Eine kurze Anleitung zur mentorierten Arbeit in Fachdidaktik wird zur Verfügung gestellt. | ||||||||||||||||||||||||||||||||||||||
| Literature | Die Literatur ist themenspezifisch. Die Studierenden beschaffen sie sich in der Regel selber (siehe Lernziele). In besonderen Fällen wird sie vom Betreuer zur Verfügung gestellt. | ||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Die Arbeit sollte vor Beginn des Praktikums abgeschlossen werden. | ||||||||||||||||||||||||||||||||||||||
| Specialized Courses in Respective Subject with Educational Focus | |||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | ||||||||||||||||||||||||||||||||||
| 272-0300-00L | Algorithmics for Hard Problems Does not take place this semester. This course d o e s n o t include the Mentored Work Specialised Courses with an Educational Focus in Computer Science A. | W | 5 credits | 2V + 1U + 1A | D. Komm | ||||||||||||||||||||||||||||||||||
| Abstract | This course unit looks into algorithmic approaches to the solving of hard problems, particularly with moderately exponential-time algorithms and parameterized algorithms. The seminar is accompanied by a comprehensive reflection upon the significance of the approaches presented for computer science tuition at high schools. | ||||||||||||||||||||||||||||||||||||||
| Learning objective | To systematically acquire an overview of the methods for solving hard problems. To get deeper knowledge of exact and parameterized algorithms. | ||||||||||||||||||||||||||||||||||||||
| Content | First, the concept of hardness of computation is introduced (repeated for the computer science students). Then some methods for solving hard problems are treated in a systematic way. For each algorithm design method, it is discussed what guarantees it can give and how we pay for the improved efficiency. A special focus lies on moderately exponential-time algorithms and parameterized algorithms. | ||||||||||||||||||||||||||||||||||||||
| Lecture notes | Unterlagen und Folien werden zur Verfügung gestellt. | ||||||||||||||||||||||||||||||||||||||
| Literature | J. Hromkovic: Algorithmics for Hard Problems, Springer 2004. R. Niedermeier: Invitation to Fixed-Parameter Algorithms, 2006. M. Cygan et al.: Parameterized Algorithms, 2015. F. Fomin et al.: Kernelization, 2019. F. Fomin, D. Kratsch: Exact Exponential Algorithms, 2010. | ||||||||||||||||||||||||||||||||||||||
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| 272-0302-00L | Approximation and Online Algorithms | W | 5 credits | 2V + 1U + 1A | H.‑J. Böckenhauer, D. Komm, M. Wettstein | ||||||||||||||||||||||||||||||||||
| Abstract | This lecture deals with approximative algorithms for hard optimization problems and algorithmic approaches for solving online problems as well as the limits of these approaches. | ||||||||||||||||||||||||||||||||||||||
| Learning objective | Get a systematic overview of different methods for designing approximative algorithms for hard optimization problems and online problems. Get to know methods for showing the limitations of these approaches. | ||||||||||||||||||||||||||||||||||||||
| Content | Approximation algorithms are one of the most succesful techniques to attack hard optimization problems. Here, we study the so-called approximation ratio, i.e., the ratio of the cost of the computed approximating solution and an optimal one (which is not computable efficiently). For an online problem, the whole instance is not known in advance, but it arrives pieceweise and for every such piece a corresponding part of the definite output must be given. The quality of an algorithm for such an online problem is measured by the competitive ratio, i.e., the ratio of the cost of the computed solution and the cost of an optimal solution that could be given if the whole input was known in advance. The contents of this lecture are - the classification of optimization problems by the reachable approximation ratio, - systematic methods to design approximation algorithms (e.g., greedy strategies, dynamic programming, linear programming relaxation), - methods to show non-approximability, - classic online problem like paging or scheduling problems and corresponding algorithms, - randomized online algorithms, - the design and analysis principles for online algorithms, and - limits of the competitive ratio and the advice complexity as a way to do a deeper analysis of the complexity of online problems. | ||||||||||||||||||||||||||||||||||||||
| Literature | The lecture is based on the following books: J. Hromkovic: Algorithmics for Hard Problems, Springer, 2004 D. Komm: An Introduction to Online Computation: Determinism, Randomization, Advice, Springer, 2016 Additional literature: A. Borodin, R. El-Yaniv: Online Computation and Competitive Analysis, Cambridge University Press, 1998 | ||||||||||||||||||||||||||||||||||||||
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| 272-0400-00L | Mentored Work Specialised Courses in the Respective Subject with Educational Focus Computer Sc A | W+ | 2 credits | 4A | D. Komm, J. Hromkovic, G. Serafini | ||||||||||||||||||||||||||||||||||
| Abstract | In the mentored work on their subject specialisation, students link high-school and university aspects of the subject, thus strengthening their teaching competence with regard to curriculum decisions and the future development of the tuition. They compile texts under supervision that are directly comprehensible to the targeted readers - generally specialist-subject teachers at high-school level. | ||||||||||||||||||||||||||||||||||||||
| Learning objective | The aim is for the students - to familiarise themselves with a new topic by obtaining material and studying the sources, so that they can selectively extend their specialist competence in this way. - to independently develop a text on the topic, with special focus on its mathematical comprehensibility in respect of the level of knowledge of the targeted readership. - To try out different options for specialist further training in their profession. | ||||||||||||||||||||||||||||||||||||||
| Content | Thematische Schwerpunkte: Die mentorierte Arbeit in FV besteht in der Regel in einer Literaturarbeit über ein Thema, das einen Bezug zum gymnasialem Unterricht oder seiner Weiterentwicklung hat. Die Studierenden setzen darin Erkenntnisse aus den Vorlesungen in FV praktisch um. Lernformen: Alle Studierenden erhalten ein individuelles Thema und erstellen dazu eine eigenständige Arbeit. Sie werden dabei von ihrer Betreuungsperson begleitet. Gegebenenfalls stellen sie ihre Arbeit oder Aspekte daraus in einem Kurzvortrag vor. Die mentorierte Arbeit ist Teil des Portfolios der Studierenden. | ||||||||||||||||||||||||||||||||||||||
| Lecture notes | Eine Anleitung zur mentorierten Arbeit in FV wird zur Verfügung gestellt. | ||||||||||||||||||||||||||||||||||||||
| Literature | Die Literatur ist themenspezifisch. Sie muss je nach Situation selber beschafft werden oder wird zur Verfügung gestellt. | ||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Die Arbeit sollte vor Beginn des Praktikums abgeschlossen werden. | ||||||||||||||||||||||||||||||||||||||
| 252-0408-00L | Cryptographic Protocols | W | 6 credits | 2V + 2U + 1A | M. Hirt | ||||||||||||||||||||||||||||||||||
| Abstract | In a cryptographic protocol, a set of parties wants to achieve some common goal, while some of the parties are dishonest. Most prominent example of a cryptographic protocol is multi-party computation, where the parties compute an arbitrary (but fixed) function of their inputs, while maintaining the secrecy of the inputs and the correctness of the outputs even if some of the parties try to cheat. | ||||||||||||||||||||||||||||||||||||||
| Learning objective | To know and understand a selection of cryptographic protocols and to be able to analyze and prove their security and efficiency. | ||||||||||||||||||||||||||||||||||||||
| Content | The selection of considered protocols varies. Currently, we consider multi-party computation, secret-sharing, broadcast and Byzantine agreement. We look at both the synchronous and the asynchronous communication model, and focus on simple protocols as well as on highly-efficient protocols. | ||||||||||||||||||||||||||||||||||||||
| Lecture notes | We provide handouts of the slides. For some of the topics, we also provide papers and/or lecture notes. | ||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | A basic understanding of fundamental cryptographic concepts (as taught for example in the course Information Security) is useful, but not required. | ||||||||||||||||||||||||||||||||||||||
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| 252-0341-01L | Information Retrieval | W | 4 credits | 2V + 1U | G. Fourny | ||||||||||||||||||||||||||||||||||
| Abstract | This course gives an introduction to information retrieval with a focus on text documents and unstructured data. Main topics comprise document modelling, various retrieval techniques, indexing techniques, query frameworks, optimization, evaluation and feedback. | ||||||||||||||||||||||||||||||||||||||
| Learning objective | We keep accumulating data at an unprecedented pace, much faster than we can process it. While Big Data techniques contribute solutions accounting for structured or semi-structured shapes such as tables, trees, graphs and cubes, the study of unstructured data is a field of its own: Information Retrieval. After this course, you will have in-depth understanding of broadly established techniques in order to model, index and query unstructured data (aka, text), including the vector space model, boolean queries, terms, posting lists, dealing with errors and imprecision. You will know how to make queries faster and how to make queries work on very large datasets. You will be capable of evaluating the quality of an information retrieval engine. Finally, you will also have knowledge about alternate models (structured data, probabilistic retrieval, language models) as well as basic search algorithms on the web such as Google's PageRank. | ||||||||||||||||||||||||||||||||||||||
| Content | 1. Introduction 2. Boolean retrieval: the basics of how to index and query unstructured data. 3. Term vocabulary: pre-processing the data prior to indexing: building the term vocabulary, posting lists. 4. Tolerant retrieval: dealing with spelling errors: tolerant retrieval. 5. Index construction: scaling up to large datasets. 6. Index compression: how to improve performance by compressing the index in various ways. 7. Ranked retrieval: how to ranking results with scores and the vector space model 8. Scoring in a bigger picture: taking ranked retrieval to the next level with various improvements, including inexact retrieval 9. Probabilistic information retrieval: how to leverage Bayesian techniques to build an alternate, probabilistic model for information retrieval 10. Language models: another alternate model based on languages, automata and document generation 11. Evaluation: precision, recall and various other measurements of quality 12. Web search: PageRank 13. Wrap-up. The lecture structure will follow the pedagogical approach of the book (see material). The field of information retrieval also encompasses machine learning aspects. However, we will make a conscious effort to limit overlaps, and be complementary with, the Introduction to Machine Learning lecture. | ||||||||||||||||||||||||||||||||||||||
| Literature | C. D. Manning, P. Raghavan, H. Schütze, Introduction to Information Retrieval, Cambridge University Press. | ||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Prior knowledge in elementary set theory, logics, linear algebra, data structures, abstract data types, algorithms, and probability theory (at the Bachelor's level) is required, as well as programming skills (we will use Python). | ||||||||||||||||||||||||||||||||||||||
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| 263-0007-00L | Advanced Systems Lab Only for master students, otherwise a special permission by the study administration of D-INFK is required. | W | 8 credits | 3V + 2U + 2A | M. Püschel | ||||||||||||||||||||||||||||||||||
| Abstract | This course introduces the student to the foundations and state-of-the-art techniques in developing high performance software for mathematical functionality occurring in various fields in computer science. The focus is on optimizing for a single core and includes optimizing for the memory hierarchy, for special instruction sets, and the possible use of automatic performance tuning. | ||||||||||||||||||||||||||||||||||||||
| Learning objective | Software performance (i.e., runtime) arises through the complex interaction of algorithm, its implementation, the compiler used, and the microarchitecture the program is run on. The first goal of the course is to provide the student with an understanding of this "vertical" interaction, and hence software performance, for mathematical functionality. The second goal is to teach a systematic strategy how to use this knowledge to write fast software for numerical problems. This strategy will be trained in several homeworks and a semester-long group project. | ||||||||||||||||||||||||||||||||||||||
| Content | The fast evolution and increasing complexity of computing platforms pose a major challenge for developers of high performance software for engineering, science, and consumer applications: it becomes increasingly harder to harness the available computing power. Straightforward implementations may lose as much as one or two orders of magnitude in performance. On the other hand, creating optimal implementations requires the developer to have an understanding of algorithms, capabilities and limitations of compilers, and the target platform's architecture and microarchitecture. This interdisciplinary course introduces the student to the foundations and state-of-the-art techniques in high performance mathematical software development using important functionality such as matrix operations, transforms, filters, and others as examples. The course will explain how to optimize for the memory hierarchy, take advantage of special instruction sets, and other details of current processors that require optimization. The concept of automatic performance tuning is introduced. The focus is on optimization for a single core; thus, the course complements others on parallel and distributed computing. Finally a general strategy for performance analysis and optimization is introduced that the students will apply in group projects that accompany the course. | ||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Solid knowledge of the C programming language and matrix algebra. | ||||||||||||||||||||||||||||||||||||||
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