Syllabus

Title
0859 Mathematics I (Science Track)
Instructors
ao.Univ.Prof. Dr. Josef Leydold
Contact details
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
11/21/18 to 11/30/18
Registration via LPIS
Notes to the course
Subject(s) Master Programs
Dates
Day Date Time Room
Wednesday 12/05/18 09:30 AM - 12:00 PM TC.3.12
Thursday 12/06/18 09:30 AM - 12:00 PM TC.5.04
Wednesday 12/12/18 09:30 AM - 12:00 PM D3.0.218
Thursday 12/13/18 09:30 AM - 12:00 PM TC.2.03
Wednesday 12/19/18 09:30 AM - 12:00 PM TC.4.03
Thursday 12/20/18 09:30 AM - 12:00 PM D3.0.222
Wednesday 01/09/19 09:30 AM - 12:00 PM D3.0.218
Thursday 01/10/19 09:30 AM - 12:00 PM D3.0.218
Wednesday 01/16/19 09:30 AM - 12:00 PM D3.0.218
Thursday 01/17/19 09:30 AM - 12:00 PM D3.0.222
Wednesday 01/23/19 09:30 AM - 12:00 PM D3.0.218
Thursday 01/24/19 09:30 AM - 12:00 PM D3.0.218
Friday 01/25/19 12:00 PM - 02:30 PM TC.5.13
Contents

Working with mathematical models requires two skills: First one needs to be familiar with techniques for handling terms and formulae and with methods for solving particular problems like finding extrema of a given function. Learning and applying such procedures is already part of the course "Mathematische Methoden". The second skill is the investigation of structural properties of a given model. One has to find conclusions that can be drawn from one's model and find convincing arguments for these.

In this course our emphasis is on mathematical reasoning. New notions are declared in definitions. Conclusions are stated in theorems. Proofs demonstrate that our claims hold in all cases where the given conditions are satisfied. Counter examples may show that a conjecture is wrong. Examples help us to deal with often abstract concepts.

We learn these ideas in the framework of linear algebra. We do this for several reasons. It provides the mathematics for all linear models which are important in, e.g., econometric studies. Moreover, in mathematics non-linear functions are often replaced by appropriate linear ones in order to make a problem tractable. The concepts in linear algebra are abstract but we often can use examples from our three dimensional world to illustrate these. Moreover, few definitions give way to rich structure with comparatively short proofs.

In summary, the course has the following topics:

  • Fundamental of mathematical reasoning
  • Definition, theorem, proof, necessary condition, sufficient condition
  • Proof techniques
  • Vector space, basis, dimension
  • Linear transformation and matrix
  • Distance, norm and Euclidean space
  • Projections
  • Determinant
  • Eigenvalues and eigenvectors

Learning outcomes

Students of this course understand fundamental principles that are indispensable for understanding higher mathematics. This includes

  • the concepts of definitions, theorems and proof
  • the important distinction between necessary and sufficient conditions
  • mathematical reasoning
  • proof techniques: direct and indirect proof, proof by contradiction

In addition they understand the basic concepts of linear algebra and can apply these to problems that occur in the analysis of linear models. A typical application are formulae used in econometrics.

Attendance requirements

For this lecture participation is obligatory. Students are allowed to miss a maximum of 20% (no matter if excused or not excused).

Teaching/learning method(s)

It is important that students learn to find their own approach to mathematics. Thus the course is organized in the following way. In each unit new concepts are presented. This includes definitions as well as some properties with prototypical proofs. In their homeworks students work on problems related to these new concepts and thus acquire both more knowledge about the mathematical concepts as well as expertise in mathematical reasoning. Ideally the students then get feedback about their solutions. Selected students will then present their solutions in the class.

Assessment

Grading is based on

  • oral presentation of homework problems (20%),
  • intermediate tests (30%),
  • final test (50%).
Readings
1 Author: Kevin Houston
Title: How to Think Like a Mathematician

Publisher: Cambridge University Press
Year: 2009
Content relevant for class examination: No
Content relevant for diploma examination: No
Recommendation: Reference literature
Type: Book
2 Author: Knut Sydsaeter, Peter Hammond, Atle Seierstad, Arne Ström
Title: Further Mathematics for Economics Analysis

Publisher: Prentice Hall
Year: 2005
Content relevant for class examination: No
Content relevant for diploma examination: No
Recommendation: Reference literature
Type: Book
3 Author: Knut Sydsaeter, Peter Hammond
Title: Essential Mathematics for Economics Analysis

Publisher: Prentice Hall
Edition: 3rd
Year: 2008
Content relevant for class examination: No
Content relevant for diploma examination: No
Recommendation: Reference literature
Type: Book
4 Author: Alpha C. Chiang and Kevin Wainwright
Title: Fundamental methods of mathematical economics

Publisher: McGraw - Hill
Edition: 4th edition
Year: 2005
Type: Book
Recommended previous knowledge and skills
Competent handling of terms, formulae, equalities and inequalities is a necessary prerequisite to master this course. 
Availability of lecturer(s)
josef.leydold@wu.ac.at
Lecture Notes
Last edited: 2018-12-03



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