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Advanced Methods in Applied Statistics PhD course
Provider: Faculty of Science

Activity no.: 5850-16-11-31 
Enrollment deadline: 05/02/2018
PlaceNiels Bohr Institute
Date and time06.02.2018, at: 00:00 - 26.04.2018, at: 00:00
Regular seats40
ECTS credits7.50
Contact personJulie Meier    E-mail address: juliemh@nbi.ku.dk
Enrolment Handling/Course OrganiserD. Jason Koskinen    E-mail address: koskinen@nbi.ku.dk
Written languageEnglish
Teaching languageEnglish
Semester/BlockBlock 3
Block noteDuration: 1 block
Scheme groupA (Tues 8-12 + Thurs 8-17)
Exam formContinuous assessment
Exam formWritten assignment, 28 hours. Assessment will be based oral presentation, graded problem sets, project(s) and final exam
Exam detailsContinuous 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%)
Exam aidsAll aids allowed
Grading scale7 point grading scale
Criteria for exam assessmentFor 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.
Exam re-examinationSame 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.
Course workload
Course workload categoryHours
Lectures36.00
Practical exercises32.00
Project work36.00
Preparation102.00
Sum206.00
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.

Literature
“Statistical Data Analysis” by G. Cowan.

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

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

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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