NFYK18000U Big Data Analysis

Volume 2019/2020
Education

MSc Programme in Physics

 

Content

The course will give the student an introduction to and a basic knowledge on Machine Learning (ML) and its use in various parts of data analysis. The focus will be on application through examples and use of computers, and be project based.

The course will cover the following subjects:

  • Introduction to Machine Learning
  • Types of problems suitable for ML and their typical solutions.
  • Types of problems not suitable for ML
  • Classification and Regression
  • Supervised vs. Unsupervised training
  • Dimensionality Reduction
  • ML performance
  • Big Data management and data access
Learning Outcome

Skills

The student should in the course obtain the following skills:

  • Understand the use of ML in data analysis
  • Use ML on a given (suitable) dataset
  • Be able to optimise the performance of the ML algorithm
  • Be capable of quantifying and comparing ML performances

Knowledge

The student will obtain knowledge about ML concepts and procedures, more specifically:

  • The fundamental methods used in ML.
  • Various Cost-Functions and Goodness measures.
  • The most commonly used ML algorithms.

Competences

This course will provide the students with an understanding of ML methods and knowledge of (structured) data analysis with ML, which enables them to analyse data using ML in science and beyond. The students should be capable of handling data sparcity, non-uniformities, and categorical data.

Literature

See Absalon for final course material.

Basic knowledge of programming is required corresponding to a bachelor course in programming for physicists.
The student should be familiar with the general line of thinking in programming, and be able to build own programs independently. Elementary mathematics (calculus, linear algebra, and combinatorics) is also needed.

Academic qualifications equivalent to a BSc degree is recommended.
Lectures, exercises by computers (mostly), discussion, and small projects.
It is expected that the student brings a laptop.
  • Category
  • Hours
  • Lectures
  • 56
  • Preparation
  • 122
  • Theory exercises
  • 28
  • Total
  • 206
Oral
Credit
7,5 ECTS
Type of assessment
Written assignment
Continuous assessment
The final grade is given based on the continuous evaluation (40%) as well as on the final project (60%).
Aid
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
several internal examiners
Re-exam

The re-exam form will be oral (30 minutes, no preparation) and include the material submitted until two weeks before the re-exam. This submitted material corresponds to the continuous evaluation and project. If some of these were already approved during the course, they can be re-used, or new projects can be submitted.

Criteria for exam assesment

see learning outcome