MySchool

Verkfræðideild
Deildarforseti:	Dr. Ármann Gylfason
Vefpóstur:	ru@ru.is
Vefsíða:	http://www.ru.is/tvd
Kennarar	Skoða

MSc í fjármálaverkfræði
Annir:	4
Ár:	2
Einingar:	120
Um námsleiðina	Fjármálaverkfræði er þverfagleg grein sem samþættir fjármálafræði við verkfræðilegar aðferðir, stærðfræðileg líkön, tölfræði, aðgerðagreiningu og hagnýta tölvunarfræði. Nám í fjármálaverkfræði hentar þeim sem hafa áhuga á t.d. áhættustýringu, afleiðuviðskiptum eða fjárstýringu. MSc námið er mótað að kröfum Verkfræðingafélags Íslands um fullnaðarmenntun í verkfræði.
Hæfniviðmið	Skoða
Námsstig	2

Táknmyndir
	Skyldufag á braut		Kennslutungumál
	Valfag á braut		Undanfarar fyrir námskeið

Vorönn/Spring 2024

Energy Financial Assessment

SE-833-FA2

6 Einingar

Verkfræðilegar bestunaraðferðir

T-423-ENOP

6 Einingar

Svefn

T-424-SLEE

6 Einingar

Ákvarðanatökuaðferðir

T-603-AKVA

6 Einingar

Starfsnám í verkfræði I

T-706-INT1

6 Einingar

Starfsnám í verkfræði II

T-706-INT2

6 Einingar

Systems Biology

T-765-SYBI

8 Einingar

Verkefnastjórnun og stefnumarkandi áætlunargerð

T-803-VERK

8 Einingar

Afleiður og áhættustýring

T-814-DERI

8 Einingar

Gerð viðskiptaáætlunar fyrir tæknilega hugmynd - Frumkvöðlafræði og nýsköpun

T-814-INNO

8 Einingar

Nýjar stefnur í fjármálaverkfræði

T-816-MTFE

8 Einingar

Vefjaverkfræði og lífefnisfræði

T-828-TISS

8 Einingar

Rannsóknarvinna í meistaranámi I

T-829-GRO1

6 Einingar

Tölvustudd greining með einingaaðferðinni

T-844-FEMM

8 Einingar

Verkfræði taugamótunar

T-863-NEUR

8 Einingar

Vindorka

T-863-WIND

8 Einingar

Rekstur raforkukerfa

T-867-POSY

8 Einingar

Stöðugleiki og stjórnun raforkukerfa

T-867-STAB

8 Einingar

Protection Philosophy for Smart-Grids

T-868-PROT

8 Einingar

Meistaraverkefni

T-899-MEIS

30 Einingar

Meistaraverkefni fyrri hluti

T-900-MEIS

30 Einingar

Meistaraverkefni seinni hluti

T-901-MEI2

30 Einingar

Business Process Management

V-716-BPMA

7,5 Einingar

Entrepreneurial Finance

V-733-ENTR

7,5 Einingar

Business Intelligence and Analytics

V-784-REK5

7,5 Einingar

Nýsköpun og stofnun fyrirtækja

X-204-STOF

6 Einingar

Skiptinám

X-699-EXCH

30 Einingar

Sumar/Summer 2024

Rannsóknarvinna í meistaranámi I

T-829-GRO1

6 Einingar

Skiptinám

X-699-EXCH

30 Einingar

Haustönn/Fall 2024

Sérhæft efni í orku I

SE-801-STE

1 Einingar

Energy Economics

SE-805-EC1

6 Einingar

Sérhæft efni í orku III

SE-806-STE

6 Einingar

Embedded System Programming

T-738-EMBE

8 Einingar

Hermun II

T-806-SIMU

6 Einingar

Gæðastjórnun

T-807-QUAL

6 Einingar

Notkun líkana við stjórnun

T-808-NOLI

8 Einingar

Gagnanám og vitvélar

T-809-DATA

8 Einingar

Bestunaraðferðir

T-810-OPTI

8 Einingar

Ár

1. ár

Önn

Haustönn/Fall 2024

Stig námsgreinar

4. Framhaldsnám, grunnnámskeið

Tegund námskeiðs

Valnámskeið

Undanfarar

T-403-ADGE, Aðgerðagreining

Skipulag

Ekkert skráð skipulag.

Kennari

Eyjólfur Ingi Ásgeirsson

Lýsing

Level of course: 3. Undergraduate (First cycle), advanced / 4. Graduate (Second cycle), introductory.
Type of course: Core in MSc Engineering Management and MSc Financial Engineering, elective in other programmes.
Mandatory prerequisites: Operation Research (T-403-ADGE).Overview and approach: This course introduces the principal algorithms for linear, network, discrete, nonlinear, dynamic optimization and optimal control. Emphasis is on methodology and the underlying mathematical structures. Topics include the simplex method, network flow methods, branch and bound and cutting plane methods for discrete optimization, optimality conditions for nonlinear optimization, interior point methods for convex optimization, Newton´s method, heuristic methods, and dynamic programming and optimal control methods.Reading material: Hillier and Lieberman, Introduction to Operations Research, 10th Edition, Pearson 2014.

Námsmarkmið

After the completion of this course students will be capable of using basic methods of Operations Research for analysing and solving complex decision problems. More specifically the student will be able to:

Understand the properties of linear optimization and how it can be used to analyze and solve complex decision problems;
Use and analyze different forms of linear optimization models;
Understand and be capable of analyzing the geometry of linear optimization;
Apply systematic methods and algoirthms for analysing and solving decision problems;
Understand the importance and usefullness of linear optimization and its applications;
Apply software to solve optimization models;
Implement solution methods for linear optimization models and have in-depth understanding of the mechanics of the Simplex methods;
Practice the use of sensitivity analysis and to derive formulas for sensitivity of model parameters;
Understand integer programming and how it can used in decision making;
Use the main solution methods for integer programming;
Understand the special properties of network models and formulate practical problems as network models;
Understand the nature of non-linear optimization problems and the challenges involved in solving the problems;
Be familiar with different classes on non-linear optimization models and some of the available solution methods and algorithms;
Understand the importance of optimization under uncertainty and be able to develop robust programming, change constraints and stochastic programming models;
Be familiar with dynamic programming;
Present results in a clear and organized manner.