NFYK15002U  Advanced Methods in Applied Statistics

Volume 2016/2017
Education

M.Sc. Physics

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.

 “Statistical Data Analysis” by G. Cowan

 

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

Instructor lectures, in-class examples, computer-based exercises, and discussion.
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.
Credit
7,5 ECTS
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
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.

Criteria for exam assesment

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.

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