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Since May 2021
Instructor since May 2021
- I provide guidance/supervision in the use of mathematical/statistical methods such as estimation, regression, testing, statistical analysis, etc. in research projects across fields such as machine learning, psychology, sociology, economics, finance, and risk management. I also assist in presenting/interpreting the results in a scientifically compliant manner.
- Additionally, I offer assistance in implementing computer code relevant to the aforementioned research projects.
This class is well-suited for MSc students who are working on their theses, as well as practitioners engaged in various research studies.
- Additionally, I offer assistance in implementing computer code relevant to the aforementioned research projects.
This class is well-suited for MSc students who are working on their theses, as well as practitioners engaged in various research studies.
I provide assistance with computer science modules that have a strong mathematical component, including predicate logic, cryptography, number theory, algorithm design, discrete and combinatorial optimization, graph theory, linear programming, machine learning, and more.
Lessons include:
- Solving specific types of problems, exercises, and past exams.
- Assisting with homework and assignments.
- Assisting with the preparation of final reports and projects.
Lessons include:
- Solving specific types of problems, exercises, and past exams.
- Assisting with homework and assignments.
- Assisting with the preparation of final reports and projects.
The class is intended for university students in their early years who are preparing for their math exams.
It covers a wide range of topics such as: calculus, analysis, (linear, vector, matrix) algebra, geometry, trigonometry, (basic) statistics, probability, combinatorics, business mathematics, as well as more abstract topics, such as topology, measure/integration theory and functional analysis.
Depending on the student's requirements, lessons may consist of:
- Providing a (comprehensive) overview of the course material, e.g. slides, lecture notes, etc.
- Solving specific (types of) problems, exercises, and (old) exams.
- Assisting with homework and assignments.
- Setting up final reports and projects.
It covers a wide range of topics such as: calculus, analysis, (linear, vector, matrix) algebra, geometry, trigonometry, (basic) statistics, probability, combinatorics, business mathematics, as well as more abstract topics, such as topology, measure/integration theory and functional analysis.
Depending on the student's requirements, lessons may consist of:
- Providing a (comprehensive) overview of the course material, e.g. slides, lecture notes, etc.
- Solving specific (types of) problems, exercises, and (old) exams.
- Assisting with homework and assignments.
- Setting up final reports and projects.
The class is intended for advanced students who are enrolled in (pre-)Master courses in one or more of the following topics: Machine Learning,
Stochastic Processes, Markov Chains, Queueing Theory, Stochastic Calculus, Stochastic Differential Equations and Applications, and Risk Management.
Stochastic Processes, Markov Chains, Queueing Theory, Stochastic Calculus, Stochastic Differential Equations and Applications, and Risk Management.
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