Business Mathematics and Informatics MSc Vrije Universiteit Amsterdam - Faculteit der Exacte Wetenschappen - M Business Mathematics and Informatics -

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1 Business Mathematics and Informatics MSc Vrije Universiteit Amsterdam - - M Business Mathematics and Informatics Vrije Universiteit Amsterdam - - M Business Mathematics and Informatics I

2 Studiegids informatie voor de masteropleiding Business Mathematics and Informatics. Klik op de onderstaande links om informatie over de vakken te bekijken. Of download de volledige studiegids als pdf met de knop Maak pdf van gehele opleiding. Vrije Universiteit Amsterdam - - M Business Mathematics and Informatics II

3 Inhoudsopgave BSociety Oriented Variant 1 Individuele vakken 1 Keuze vakken Mathematics and BMI 2 Verplichte vakken 2 BMI Dual Master's Programme 3 Individuele vakken 4 Master BMI The Dual Master's Programme - keuzevakken Mathematics and BMI 4 BMI Duaal verplichte vakken 5 Vak: Applied Analysis: Financial Mathematics 5 Vak: Applied Stochastic Modeling 6 Vak: Business Intelligence 7 Vak: Business Mathematics and Informatics Paper 8 Vak: Caput Optimization of Business Proc. 8 Vak: Combinatorial Optimization 9 Vak: Combinatorische optimalisatie 10 Vak: Corporate Financial Management 10 Vak: Data Mining Techniques 11 Vak: Discrete wiskunde 12 Vak: Dual Workperiod Master 13 Vak: Evolutionary Computing 13 Vak: FEW individueel vak intern 14 Vak: History and Philosophy of the Information Society 14 Vak: Investments 15 Vak: Master Project BMI 16 Vak: Mathematical System Theory 17 Vak: Neurale Netwerken 17 Vak: Numerical Methods 18 Vak: Optimization of Business Processes 18 Vak: Performance Analysis of Communication Networks 19 Vak: Project Optimization of Business Processes 20 Vak: Scientific Writing in English 20 Vak: Services Logistics 22 Vak: Statistical Models 23 Vak: Stochastic Optimization 23 Vak: Stochastic Processes for Finance 24 Vrije Universiteit Amsterdam - - M Business Mathematics and Informatics III

4 BSociety Oriented Variant This is the standard Master's programme. The goal of the programme is to prepare students for a career in business, industry, or within governmental or research facilities, with (possibly only initially) a major quantitative aspect. The emphasis will be on a broad and multidisciplinary education, preparing the student for a role as an academically trained quantitative professional in a multidisciplinary organisation. Even so, the possibility to continue after the Master in a PhD programme exists also for this variant. There are at present three specializations possible: Business Process Optimization This subject underscores the multidisciplinary nature of BMI: you will tackle quantitative business problems with the aid of mathematical algorithms which are then implemented in decision-support systems. All aspects of this subject are addressed from the perspective of interrelationships. Computational Intelligence Computational Intelligence (CI) is a collective name for various fields of application and problem-solving techniques. Typical CI applications are optimization and data mining. CI offers a succesful pragmatic approach to actual problems. Financial Risk Management This specialization prepares you for a quantitative position in the financial world. The subjects covered include the pricing of derivatives, such as share options, and risk management. You will deal with both practical and theoretical aspects of the discipline. The progamme consists of 120 credits - compulsory courses 72 credits (including a Master Project of 36 credits) - compulsory optional choice 30 credits - optional courses 18 credits Note: Every programme, including the choice of optional courses, has to be discussed and agreed upon with the master coordinator or a personal mentor and approved by the Examination Board. Opleidingsdelen: Individuele vakken Keuze vakken Mathematics and BMI Verplichte vakken Individuele vakken Vrije Universiteit Amsterdam - - M Business Mathematics and Informatics Pagina 1 van 25

5 Vakken: Naam Periode Credits Code FEW individueel vak intern 6.0 X_INDVAKI_09 Keuze vakken Mathematics and BMI Students can not choose both the courses Combinatorial Optimization and Combinatorische optimalisatie. Note: Every programme, including the choice of optional courses, has to be discussed and agreed upon with the master coordinator or a personal mentor and approved by the Examination Board. Vakken: Naam Periode Credits Code Business Intelligence Period E_BK3_BI Caput Optimization of Business Proc. Verplichte vakken 6.0 X_ Combinatorial Optimization Period E_EORM_CO Combinatorische optimalisatie Corporate Financial Management Periode X_ Period E_BK3_CFM Discrete wiskunde Periode X_ Evolutionary Computing Period X_ Investments Period E_EBE3_INV Mathematical System Theory Period X_ Neurale Netwerken Periode X_ Numerical Methods Periode X_ Optimization of Business Processes Performance Analysis of Communication Networks Period X_ Period X_ Services Logistics Period E_BK3_SL Statistical Models Period X_ Stochastic Optimization Period X_ Stochastic Processes for Finance Periode X_ Compulsory alongside the mentioned courses, are a compulsory optional choice (30 credits) and optional courses (18 credits) to complete 120 credits. Vrije Universiteit Amsterdam - - M Business Mathematics and Informatics Pagina 2 van 25

6 Note: Every programme, including the choice of optional courses, has to be discussed and agreed upon with the master coordinator or a personal mentor and approved by the Examination Board. Vakken: Naam Periode Credits Code Applied Analysis: Financial Mathematics BMI Dual Master's Programme Period X_ Applied Stochastic Modeling Period X_ Business Mathematics and Informatics Paper Ac. Year (September) 6.0 X_ Data Mining Techniques Period X_ History and Philosophy of the Information Society Period X_ Master Project BMI Ac. Jaar (september) 36.0 X_ Project Optimization of Business Processes Scientific Writing in English Periode 2, Periode 3, Periode 4, Periode 5 Period X_ X_ The dual Master's programme combines work and study. During this programme the student is employed part time, and studies part time. The work has to be relevant for the study. The external master's project is incorporated in the work, although supervision of the external master's project has to involve at least one university staffmember. In addition the student gets some credit points for the work time. Moreover, it is possible to do the BMI paper (6 cp) on a case-study that is work related, provided the case-study is combined with a sound theoretical basis. This makes the total of work related credit points, including the external master's project, 60, being the equivalent of one year of study. Typically, a student participating in the dual master's programme should expect to obtain the Master's diploma after two and a half years. Admission to the dual Master's programme is granted to those who have a BMI Bachelor's degree. For those with another univerisity Bachelor's degree, such as Mathematics, Econometrics, Computer Science, or a Bachelor's degree from an institute of higher education, admission may be granted on an individual basis. Those seeking admission to the dual Master's programme should realise that admission also depends on obtaining suitable employment. The VU has contacts with a number of companies that are interested in participating in this programme. For more information concerning the dual master's programme, contact the coordinator for the external master's project, or the master coordinator. The progamme consists of 120 credits - compulsory courses 84 credits (including a Master Project of 36 credits) - compulsory optional choice 24 credits Vrije Universiteit Amsterdam - - M Business Mathematics and Informatics Pagina 3 van 25

7 - optional courses 12 credits Note: Every programme, including the choice of optional courses, has to be discussed and agreed upon with the master coordinator or a personal mentor and approved by the Examination Board. Opleidingsdelen: Individuele vakken Master BMI The Dual Master's Programme - keuzevakken Mathematics and BMI BMI Duaal verplichte vakken Individuele vakken Vakken: Naam Periode Credits Code FEW individueel vak intern 6.0 X_INDVAKI_09 Master BMI The Dual Master's Programme - keuzevakken Mathematics and BMI Students can not choose both the courses Combinatorial Optimization and Combinatorische optimalisatie. Note: Every programme, including the choice of optional courses, has to be discussed and agreed upon with the master coordinator or a personal mentor and approved by the Examination Board. Vakken: Naam Periode Credits Code Business Intelligence Period E_BK3_BI Combinatorial Optimization Period E_EORM_CO Combinatorische optimalisatie Corporate Financial Management Periode X_ Period E_BK3_CFM Discrete wiskunde Periode X_ Evolutionary Computing Period X_ Investments Period E_EBE3_INV Mathematical System Theory Period X_ Neurale Netwerken Periode X_ Numerical Methods Periode X_ Optimization of Business Processes Performance Analysis of Communication Networks Period X_ Period X_ Vrije Universiteit Amsterdam - - M Business Mathematics and Informatics Pagina 4 van 25

8 Project Optimization of Business Processes BMI Duaal verplichte vakken Period X_ Services Logistics Period E_BK3_SL Statistical Models Period X_ Stochastic Optimization Period X_ Stochastic Processes for Finance Periode X_ Both the 'BWI werkstuk' and the Master Project may be work-related. The work period consists of 18 credits, the total work related credit points has therefore a maximum of 60 credits. Compulsory alongside the mentioned courses, are a compulsory optional choice (24 credits) and optional courses (12 credits) to complete 120 credits. Note: Every programme, including the choice of optional courses, has to be discussed and agreed upon with the master coordinator or a personal mentor and approved by the Examination Board. Vakken: Naam Periode Credits Code Applied Analysis: Financial Mathematics Applied Analysis: Financial Mathematics Period X_ Applied Stochastic Modeling Period X_ Business Mathematics and Informatics Paper Ac. Year (September) 6.0 X_ Data Mining Techniques Period X_ Dual Workperiod Master Ac. Jaar (september) 18.0 X_ History and Philosophy of the Information Society Period X_ Master Project BMI Ac. Jaar (september) 36.0 X_ Scientific Writing in English Periode 2, Periode 3, Periode 4, Periode X_ Course code X_ (400076) Period Period 1+2 English prof. dr. A.C.M. Ran prof. dr. A.C.M. Ran Lecture Vrije Universiteit Amsterdam - - M Business Mathematics and Informatics Pagina 5 van 25

9 Course content This course gives an introduction to financial mathematics. The following subjects will be treated: - introduction in the theory of options; - the binomial method; - introduction to differential equations; - the heat equation; - the Black-Scholes formula and applications; - introduction to numerical methods, approximating the price of an (American) option. Form of tuition Lectures, exercises with computer, discussion of exercises. Type of assessment Homework exercises and oral examination Lecture notes. Entry requirements For participation to the exam: Calculus I (X_400573) and Calculus II (X_ ) Target group 3W, mmath, mbmi, 3Ect Remarks It is possible to do some additional work by elaborating on a specific subject from the theory of partial differential equations or financial mathematics. Applied Stochastic Modeling Course code X_ (400392) Period Period 1+2 English prof. dr. G.M. Koole prof. dr. G.M. Koole Lecture, Seminar Course content This course deals with a number of stochastic modeling techniques that are often used in practice. They are motivated by showing the business context in which they are used. Topics we deal with are: birth-death processes, basic queueing models, inventory models, renewal theory and simulation. We also repeat and extend certain parts of probability theory. We end with an overview of mathematical modeling, including aspects such as the economic context, the choice of solution method, decision support systems, etc. Vrije Universiteit Amsterdam - - M Business Mathematics and Informatics Pagina 6 van 25

10 Type of assessment Written examination. Target group mbmi, mmath Business Intelligence Course code E_BK3_BI ( ) Period Period 1 Dutch Fac. der Economische Wet. en Bedrijfsk. dr. J.F.M. Feldberg dr. J.F.M. Feldberg Lecture, Practical Course objective Students that have successfully accomplished this course will: - Have an academic attitude towards business intelligence (BI) and decision support systems theories and business issues. - Have the appropriate knowledge to sensibly think about decision support systems and BI solutions in an organizational context (design, development, implementation and evaluation). - Have the skills to work with a popular decision support tool (Cognos Powerplay). By means of 'learning by doing' elementary skills in the usage of decision support systems are acquired. - Be able to identify the (break through) opportunities of BI solutions in realizing sustainable competitive advantage. - Be able to participate in project teams that decide on the design, development, implementation, and use of BI solutions. - Be able to apply scientific theories on decision support systems in an organizational context. - Have the appropriate knowledge and skills to self- reliantly deepen their knowledge on BI solutions and decision support systems. Course content Modern organizations, in particular the management of these organizations, tend to suffer more from an overload of data than from a lack of data. To a great extent this overload is caused by the overwhelming growth of information systems in organizations. Enterprise Systems (ERP), Customer Relationship Systems (CRM) as well as the growing number of Internet- based applications (e. g. e- commerce) are all important sources for the explosion of financial, production, marketing and other business data. The challenge for most organizations is to develop and build systems that support the transformation of the collected data into knowledge. To be successful in this transformation processes organizations have to develop the capability to aggregate, analyze and use data to make informed decisions. This course deals with the theory concerning business intelligence as well as with the application of business intelligence solutions. To be able to successfully implement business intelligence solutions, one has to have knowledge about their functioning and proficiency in using them, as well as knowledge about their field of application, e. g., how to Vrije Universiteit Amsterdam - - M Business Mathematics and Informatics Pagina 7 van 25

11 select, transform, integrate, condense, store and analyze relevant data. This course uses the term 'business intelligence' in a broad sense. A narrow interpretation would only deal with software solutions ('data warehousing' and 'online analytical processing'). The broad interpretation - to be used in this course - also includes: theories concerning decision making, related decision support systems and their application for management, i. e., data warehousing, online analytical processing and data mining. Form of tuition lecture tutorial Type of assessment written interim examination (weekly) Business intelligence tutorial tests. All tests and exams will be administered through a digital test system. - To be announced. - Various papers. Recommended background knowledge - Basic course in Information Systems, f. e. on the level of Laudon & Laudon, Management Information Systems, Managing the Digital Firm. 9th edition. Prentice Hall, O'Brien, James A., Introduction to Information Systems. 12th edition. Mc Graw Hill, Business Mathematics and Informatics Paper Course code X_ (400206) Period Ac. Year (September) English Course objective The objective of the report is to demonstrate the student's ability to describe a problem in a clear manner (the report should therefore be concise and 'to the point') for the benefit of an expert manager. Type of assessment A written report and a verbal presentation. Caput Optimization of Business Proc. Vakcode X_ (400260) Voertaal Engels Faculteit Coördinator prof. dr. G.M. Koole Vrije Universiteit Amsterdam - - M Business Mathematics and Informatics Pagina 8 van 25

12 Combinatorial Optimization Course code E_EORM_CO ( ) Period Period 1 English Fac. der Economische Wet. en Bedrijfsk. prof. dr. L. Stougie prof. dr. L. Stougie Lecture Course objective This course is an introduction into combinatorial optimization. It is algorithmically oriented. Theoretical correctness and running time analysis of algorithms is one of the main items of the course. The basis of computational complexity is studied in order to distinguish between well-solved and hard combinatorial optimisation problems. Efficient solution methods for well-solved problems and approximation methods for hard problems are studied. In the latter case, performance guarantees are derived. Goals for the students to achieve: - To acquire the skills for proving basic theorems in graph theory and combinatorial optimisation. - Be able to distinguish theoretically between easy and hard problems and prove some easy NP-hardness. - Learning a wide variety of combinatorial optimization models. - Learn efficient algorithms for general well-solved problems - Learn approximation techniques for computationally hard problems Course content Combinatorial Optimization deals with situations where the best alternative has to be selected from a finite set. This may seem trivial. However, the number of elements may be huge, and it may be far from easy to find the best or even a good solution. A rich class of Combinatorial Optimisation problems comes from Graph and Network Optimisation. The course starts therefore with a quick introduction into graph theory. We start with studying Matching and Flow problems, proving properties that help to develop the algorithms with which to solve these problems efficiently. Specifically we determine the running time of the algorithms. We cover Combinatorial Optimization models, such as the Travelling Salesman Problem, Steiner Trees, Set Covering, etc., which are important for many practical problems related to transportation, telecommunication, production and activity planning. Then we study the foundations of the computational complexity theory to find out that not all combinatorial optimisation problems are efficiently solvable. Next to this insight, we use it to present a wide variety of Combintorial Optimisation problems. Approximation algorithms for computationally hard problems are the subject of the last part of the course. The design and performance analysis of such algorithms are central themes. As an example we study a general technique using ILP, LP-relaxations and rounding. Vrije Universiteit Amsterdam - - M Business Mathematics and Informatics Pagina 9 van 25

13 Type of assessment written examination at the end of the course Papadimitriou, H. Christos & Steiglitz, Combinatorial Optimization (Algorithms And Complexity). Kenneth Dover Publications, 1999, ISBN paperback. Entry requirements Bedrijfseconometrie I, period 3.1 Combinatorische optimalisatie Vakcode X_ (401067) Periode Periode 2 Voertaal Nederlands Faculteit Coördinator dr. ir. R.A. Sitters Docent(en) dr. ir. R.A. Sitters Lesmethode(n) Hoorcollege, Werkcollege Corporate Financial Management Course code E_BK3_CFM ( ) Period Period 4 Dutch Fac. der Economische Wet. en Bedrijfsk. ir. F.W. van den Berg prof. dr. A.B. Dorsman Lecture, Course objective This course expands on financial topics covered in the first and second year. The emphasis in this course is on the Optimal Capital Structure of a corporation. The aim is to prepare students for a (possible) career as (assistant) Financial Manager in Industry or in the FBI sector: Finance, Banking (commercial and investment) and Insurance, incl. pension funds, investments funds, stock markets, Euronext, DNB, ECB, AFM, Ministry of Finance etc. Course content The following topics, issues and concepts will be dealt with: - Review of Valuation and Pricing of Risk - Capital structure in perfect Markets - Leverage and Debt - Optimal Capital Structure with Taxes and Financial Distress - Payout Policy, Dividends and Share Repurchases - Capital budgeting and Valuation Vrije Universiteit Amsterdam - - M Business Mathematics and Informatics Pagina 10 van 25

14 - Financial Modeling - Corporate Governance Form of tuition lecture Students have to complete before each lecture quizzes (tests) on MyFinancLab. Type of assessment written interim examination (80% 5, 0 min. ) cases / tutorials (20% of final grade based on average of scores of tests and quizzes on MyFinanceLab. Berk & DeMarzo. Corporate Finance. 1st (or 2nd) edition. Pearson / Addison Wesley. This book has an elaborate digital support service (MyFinanceLab) and is therefore used in all Bachelor Finance courses: Fin.1. 5 (ECO), Fin. Mgt.1. 5 (BK), Fin.2. 2 (ECO), Fin. Mgt.2. 2 (BK/IBA), Fin 2. 5 (ECO), Fin. Mod.2. 2 (ECO), Corp. Fin.3. 2 (ECO), Corp. Fin. Mgt.3. 4 (BK). Students must buy the book NEW to obtain entry to MyFinanceLab (the book can then be used for all bachelor years). If the book has been bought before, students must renew their subscription on the VU portal. A second hand book cannot be used. Entry requirements This course is for Business Administration students and/or Pre- Master BK students specializing in Financial Management. Students must be familiar with Corporate Finance / Financial Management as covered in the 1st and 2nd year. Pre- master students (from a finance, economics, accounting or equivalent background) must familiarize themselves with this material beforehand. This is not a basic finance course. Fundamental knowledge of financial accounting, financial management and corporate finance is a prerequisite. Data Mining Techniques Course code X_ (400108) Period Period 5 English dr. Z. Szlavik dr. Z. Szlavik Lecture Course objective The aim of the course is that students acquire data mining knowledge and skills that they can apply in a business environment. How the aims are to be achieved: Students will acquire knowledge and skills mainly through the following: an overview of the most common data mining algorithms and techniques (in lectures), a survey of typical and interesting data mining applications, and practical assignments to gain "hands on" experience. The application of skills in Vrije Universiteit Amsterdam - - M Business Mathematics and Informatics Pagina 11 van 25

15 a business environment will be simulated through various assignments of the course. Course content The course will provide a survey of basic data mining techniques and their applications for solving real life problems. After a general introduction to Data Mining we will discuss some "classical" algorithms like Naive Bayes, Decision Trees, Association Rules, etc., and some recently discovered methods such as boosting, Support Vector Machines, and co-learning. A number of successful applications of data mining will also be discussed: marketing, fraud detection, text and Web mining, possibly bioinformatics. In addition to lectures, there will be an extensive practical part, where students will experiment with various data mining algorithms and data sets. The grade for the course will be based on these practical assignments (i.e., there will be no final examination). Form of tuition Lectures and compulsory practical work. Lectures are planned to be interactive: there will be small questions, one-minute discussions, following an algorithm on paper, looking for patterns in a dataset about you (!), filling in missing pieces in a table, coming up with a number of creative solutions to a small problem, etc. Type of assessment Practical assignments (i.e. there is no exam). There will be three assignments, some (parts) of these will be done individually, some in groups of two. There is a possibility to get a grade without doing these assignments: one (!) group can be selected (based on interviews conducted by the lecturer) to do a real research project instead (which - be warned - will most likely to involve more work, but it can also be more rewarding). Ian H. Witten, Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufman, (Second Edition) Additionally, a collection of articles in electronic form (BB). Entry requirements Kansrekening en Statistiek of Algemene Statistiek (knowledge of statistics and probabilities) or equivalent. Recommended: Machine Learning. Target group mbmi, mcs, mai, mbio Discrete wiskunde Vakcode X_ (400582) Periode Periode 1+2 Voertaal Nederlands Faculteit Coördinator dr. M.L.J. van de Vel Docent(en) dr. M.L.J. van de Vel Lesmethode(n) Hoorcollege, Werkcollege Vrije Universiteit Amsterdam - - M Business Mathematics and Informatics Pagina 12 van 25

16 Inhoud vak Grafentheorie (Euler- en Hamiltongrafen, bomen). Stromen in netwerken met algoritmen, optimalisatie toepassingen in scheduling, grafentheorie, enzovoort. Teltheorie met genererende functies en inclusie/exclusie. Een eerste inleiding in de coderingstheorie ontwerpen van Hamming codes. Onderwijsvorm Hoorcollege met practicum. Toetsvorm Schriftelijk tentamen met voortentamen. Literatuur Dictaat "Combinatoriek, grafentheorie en getaltheorie" (open universiteit). Nota's "coderingstheorie" worden beschikbaar gesteld. Aanbevolen voorkennis Veronderstelde voorkennis: Calculus I (400300), Calculus II (400301), Algebra I, Lineaire Algebra. Doelgroep 3W, mbmi Dual Workperiod Master Vakcode X_ (400460) Periode Ac. Jaar (september) Credits 18.0 Voertaal Engels Faculteit Inhoud vak During two years, students are required to divide their time equally between work and study. So study and work are fully integrated. The student is an employee and a student at the same time. The student is on the payroll of the host organization. The student will conduct work which is of direct relevance to the BMI master study programme. Onderwijsvorm The student is an employee of the host organization. Overige informatie For more information on the trainee program: -bwi-studeren/index.asp Evolutionary Computing Course code X_ (400111) Period Period 1 English Vrije Universiteit Amsterdam - - M Business Mathematics and Informatics Pagina 13 van 25

17 prof. dr. A.E. Eiben prof. dr. A.E. Eiben Lecture Course objective To learn about computational methods based on Darwinian principles of evolution. To illustrate the usage of such methods as problem solvers and as simulation, respectively modelling tools.to gain hands-on experience in performing experiments. Course content The course is treating various algorithms based on the Darwinian evolution theory. Driven by natural selection (survival of the fittest), an evolution process is being emulated and solutions for a given problem are being "bred". During this course all "dialects" within evolutionary computing are treated (genetic algorithms, evolutiestrategieën, evolutionary programming, genetic programming, and classifier systems). Applications in optimisation, constraint handling and machine learning are discussed. Specific subjects handled include: various genetic structures (representations), selection techniques, sexual and asexual genetic operators, (self-)adaptivity. If time permits, subjects in Artificial Life and Artificial Societies, and Evolutionary Art will be handled. Hands-on-experience is gained by a compulsory pogramming assignment. Form of tuition Oral lectures and compulsory pogramming assignment. Type of assessment Written exam and pogramming assignment (weighted average). Eiben, A.E., Smith, J.E., Introduction to Evolutionary Computing. Springer, 2003 ISBN Slides available from Target group mbmi, mai, mcs, mpdcs FEW individueel vak intern Vakcode X_INDVAKI_09 () Faculteit History and Philosophy of the Information Society Course code X_ (405043) Period Period 2 Credits 3.0 English Vrije Universiteit Amsterdam - - M Business Mathematics and Informatics Pagina 14 van 25

18 dr. ir. T. Koetsier dr. ir. T. Koetsier Lecture Course content This course consists of two parts. Part one (3 credit points) is devoted to an "Introduction to the History and the Philosophy of the Information Age". Part one is taught by Teun Koetsier. Part two (1 credit point), devoted to the legal aspects of the information society, is taught by lawyers from the Computer/Law Institute of the Vrije Universiteit Form of tuition Lectures ( in English). Type of assessment Written exam (in English). To be announced on the Blackboard page of the course. Digital material will be made available there as well. Entry requirements None. Target group 2-IMM, 3-IMM, mai, mbmi, mcs, mis Investments Course code E_EBE3_INV ( ) Period Period 4 English Fac. der Economische Wet. en Bedrijfsk. dr. D.G. Stefanova dr. D.G. Stefanova Lecture Course objective This course aims to make students familiar with the insights from investments and portfolio management theory. Students also have to be able to apply these insights in practical situations involving portfolio decisions and investment management for both individuals and institutions. Course content Investment decisions take a prominent role in everyday life. We can think of investment decisions taken by institutional investors (banks, insurance companies, pension funds, mutual funds), but also financial decisions taken by individual households (additional pension savings, saving for ones children's education (and how), buying a house, etc.) Investment theory is also strongly linked with risk management. The importance of sound decision making in this field has been underlined by Vrije Universiteit Amsterdam - - M Business Mathematics and Informatics Pagina 15 van 25

19 recent experiences on financial markets, law suits involving complex financial products for retail clients, the debate about the (in)solidity of pensions, etc. The Investments course aims to provide an overview of the principles of investment analysis. A framework is developed that allows one to address a variety of (at first sight) completely different investment problems in a unified way. The theoretical underpinnings are developed from modern portfolio theory, with mean-variance optimization and the CAPM as key ingredients. The second component of the course deals with the empirical research for financial markets and the actual mechanisms driving these markets. Factor models for returns on financial products are very important here. The third component consists of valuation and risk attribution (including performance attribution) for individual financial products as well as portfolios of these products. Form of tuition lecture and case assignments Type of assessment Written exam Case work Exam questions are meant to test the candidate's theoretical insight as well as analytical and computational skills. Case work is used to test students implementation skills in Excel of the material treated in the course. Correctly completing a minimum of case work is compulsory for obtaining a pass for this course. Guidelines are communicated via Blackboard at the start of the course. - Bodie, Z., A. Kane, & A.J. Marcus, Investments. 8th edition, 2009, McGraw Hill - Troy Adair, Excel Applications for Investments. 2005, McGraw Hill Entry requirements Students are expected to be familiar with: - Economics and Business Economics students: Finance 1.5, 2.2 and 2.5; Quantitative Methods 1.2; Research Methods for Economics and Finance 3.1 -(International) Business Administration students (BK/IBA): Finance and Financial Arithmetic, Financial Management 2.4, Quantitative Business Analysis, Statistics; Research Methods for Economics and Finance Students are at an advantage if they also completed Financial Modelling 2.2 Remarks The course is taught in English Master Project BMI Vakcode X_ (400459) Periode Ac. Jaar (september) Credits 36.0 Voertaal Engels Faculteit Overige informatie If you are planning to start your Master Project within four months, please make an appointment with Annemieke van Goor Vrije Universiteit Amsterdam - - M Business Mathematics and Informatics Pagina 16 van 25

20 More information: r-project-bwi/index.asp and handleiding/index.asp. Mathematical System Theory Course code X_ (400180) Period Period 4+5 English prof. dr. J.H. Schuppen prof. dr. A.C.M. Ran Lecture Course content Many phenomena are characterized by dynamic behaviour where we are interested in a certain input/output behaviour. Examples are to be found in the exact and natural sciences (mechanics, biology, ecology), in engineering (air- and spacecraft design, mechanical engineering) as well as in economics and econometrics (macro- economical models, conjucture, trend and seasonal influences in demand and supply, production systems). Systems theory is concerned with modeling, estimation and control of dynamical phenomena. During the course the following subjects will be treated: models and representations (linear systems, input-output, state space, transfer function, stochastic systems, spectrum), control (stabilisation, feedback, pole placement, dynamic programming, the LQ problem), and identification and prediction (parameter estimation, spectral analysis, Kalman- filter, model reduction). Applications are in the area of optimal control and prediction. Form of tuition There is a lecture of two hours each week. In addition, there is a onehour practicum, in which there is the possibility to ask questions about the compulsary computerpracticum. The practicum makes use of the Matlab package. Type of assessment The computerpracticum counts for 70 %, the oral examination concerns the theory and counts for 30 %. Chr. Heij, A.C.M. Ran and F. van Schagen, Introduction to Mathematical Systems Theory, Birkhauser Verlag Target group 3W, mbwi, mmath Neurale Netwerken Vakcode X_ (400132) Vrije Universiteit Amsterdam - - M Business Mathematics and Informatics Pagina 17 van 25

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