Kursussøgning, efter- og videreuddannelse – Københavns Universitet

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Kursussøgning, efter- og videreuddannelse

Advanced Methods in Applied Statistics

Practical information
Study year 2016/2017
Time
Block 3
Programme level Full Degree Master
ECTS 7,5 ECTS
Course responsible
  • D. Jason Koskinen (8-6d71756d6b7067704270646b306d7730666d)
  • The Niels Bohr Institute
Course number: NFYK15002U

Course content

The course will offer the practical knowledge and hands-on experience in computational analysis of data in all frontier physics research, with particular relevance for particle physics, astrophysics, and cosmology. Lectures, examples, and exercises will be administered via computer demonstration, mainly using the python or C/C++ coding languages.

 

A subset of the course will focus on the analysis features relevant to the specific graduate research topics and interests of the enrolled students.

Learning outcome

Knowledge:

  • Be familiar with multiple machine learning algorithms and multivariate analysis techniques
  • Understand the biases and impacts of various confidence interval methods
  • Understand Bayesian and Frequentist approaches to interpreting data and the limits of assumed priors
  • Minimization techniques such as hill climbing methods, flocking algorithms, and simulated annealing

 

Skills:

  • Maximum Likelihood fitting
  • Construction of Confidence Intervals (Poisson, Feldman-Cousins, a priori and a posteriori p-values, etc.)
  • Apply computational methods to de-noise data and images
  • Code a chi-squared function in the language of the students preference (Python, C/C++, Ruby, JAVA, R, etc)
  • Creation and usage of spline functions
  • Application of Kernel Density Estimators

 

Competences:

This course will provide the advanced computational tools for data analysis related to manuscript preparation, thesis writing, and understanding the methodology and statistical relevance of results in journal articles. The students will have enhanced general coding skills useful in the both academia and industry.

Remarks

It is expected that students bring their own laptops or have access to a computer upon which they can install software to write, compile, and execute code.

There will be an introduction the week before the course begins to address software requirements and any additional course logistics.

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Education

M.Sc. Physics

Studyboard

Study Board of Physics, Chemistry and Nanoscience

Course type

Single subject courses (day)

Duration

1 block

Schedulegroup

A
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Teaching and learning methods

Instructor lectures, in-class examples, computer-based exercises, and discussion.

Capacity

no limit

Language

English

Literature

 “Statistical Data Analysis” by G. Cowan

 

Class lecture notes and links to scholarly articles will be posted online.

Workload

Category Hours
Lectures 36
Practical exercises 32
Project work 36
Preparation 102
English 206

Exam

Type of assessment

Continuous assessment
Written assignment, 28 hours
Assessment will be based on:
- An in-class short oral presentation (10%)
- Graded problem sets and project(s) centering around the coding, implementation, and execution of a statistical method (50%)
- Take home final exam (40%)

Aid

All aids allowed

Marking scale

7-point grading scale

Criteria for exam assessment

For a 12, a student must display mastery of an orally presented topic including accurate answers to follow-up questions, in addition to the contributions from graded problems sets, projects, and take home exam. The final assessment will be a total of all components, with no minimum requirement for any of the individual criteria.

Censorship form

No external censorship

Exam period

More internal examiners

Re-exam

Same as ordinary take home exam, which must be on a different topic and approved by the instructor(s).
Points from oral presentation and problem sets are carried over to the re-exam. If these points are not sufficient to make it possible to pass the re-exam, a number of problem sets can be re-submitted no later than two weeks before the re-exam.

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