Statistical Inference for Industrial Analytics
Syllabus, Master's level, 1TS322
- Code
- 1TS322
- Education cycle
- Second cycle
- Main field(s) of study and in-depth level
- Industrial Engineering and Management A1N
- Grading system
- Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
- Finalised by
- The Faculty Board of Science and Technology, 2 March 2021
- Responsible department
- Department of Civil and Industrial Engineering
Entry requirements
150 credits, including 10 credits in computer programming, 5 credits in scientific computing, 20 credits in mathematics, and 5 credits in statistics and probability theory. Proficiency in English equivalent to the Swedish upper secondary course English 6.
Learning outcomes
On completion of the course the student shall be able to
- apply computer intensive statistical inference methods based on resampling and Bayesian based methods for analysis of one and many variables, as alternatives to frequentistic methods, for problems relevant in Industrial Analytics,
- explain pros and cons regarding data modelling and algorithmic modelling (machine learning), respectively, for statistical inference problems in Industrial Analytics,
- generate relevant simulated univariate, multivariate and temporal data sets commonly occurring within the area of Industrial Analytics.
- explain about experimental design methods including active machine learning for problems relevant in Industrial Analytics.
Content
Classical, resampling and Bayesian based statistical inference for hypothesis testing, interval estimation, parameter estimation, variable selection, model selection and prediction. Comparison of data modelling with algorithm modelling (machine learning) approaches to statistical inference. Simulation of data from urn models, mixtures of probability distributions, multivariate distributions, linear and nonlinear regression models, Markov models, Hidden Markov Models, including additive/multiplicative experimental noise. Experimental design methodologies including optimal experimental design, practical approximations, and active machine learning.
Instruction
Lectures, seminars, laboratory sessions, and supervision of project
Assessment
Written examination, and written and oral presentation of project work.
If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding targeted pedagogical support from the disability coordinator of the university.