course-details-portlet

TTK4260 - Multivariate analysis and Machine learning methods

About

Examination arrangement

Examination arrangement: Written examination
Grade: Letters

Evaluation Weighting Duration Grade deviation Examination aids
Written examination 100/100 4 hours D

Course content

Theory and applications of data analytical methods that are instrumental for the work of control engineers (see the intended learning outcomes for a summary of which ones). Overview and demonstration of the capabilities of other machine-learning oriented data analysis methods (again, listed in the intended learning outcomes). Discussions about how to structure the data analysis workflows, how to interpret the data and the results, and how to contextualize the various approaches so to be able to select the appropriate ones and motivate the selection. The course will thus have two distinct "working modes": one where the theory of the algorithms will be presented in detail, and one where the algorithms will be introduced and demonstrated without deriving them in detail. The first part deals with tools that are within the core knowledge that control engineers shall have. The second deals with ancillary tools and provides an overview of the possibilities offered by the current state-of-the-art methods within Machine Learning.

Learning outcome

Intended cognitive learning outcomes

* Refreshing background knowledge:

- motivations and underlying points of view

- overview of the data analysis methods types and traditions

- Least Squares

- Maximum Likelihood and Maximum a posteriori

- statistical performance indexes

- bias vs variance trade-off

 

* Detailed analysis of the following algorithms / methods:

- PCA, ICA, PCR, PLS, multiblock and PARAFAC algorithms

- outlier detection

- time series prediction

- time series models identification

- model order selection

- design of experiments

 

* Overview and demonstration of the capabilities / working strategies of:

- change detection

- IDLE methods

- subspace identification

- Neural networks

- Random Forests

- Support vector machines

- t-SNE

- other clustering methods (especially Nearest Neighbours)

- other classification methods

Intended non-cognitive learning outcomes:

- understand the philosophies, strengths, and limitations of the various methods

- knowing the meaning of the data and the interpretation of the data

- contextualising the learned strategies and understanding how to combine them

- become independent, self-confident, and critical with regards to data analysis

 [1] K. Poynton, Cognitive and non-cognitive learning factors, http://cim.acs-schools.com/wp-content/uploads/2015/08/Cognitive-and-non-cognitive-learning-factors.pdf

Learning methods and activities

- both frontal lectures and flipped classrooms

- peer instruction sessions

- interactive data analysis sessions

- at-home data analysis projects on some preassigned datasets

Compulsory assignments

  • compulsory exercise

Further on evaluation

The written exam provides the basis for the final grade in the course. In case of postponed examination (continuation examination), the written examination may be changed to oral examination. If, after the postponed examination, the student has not yet passed the exam, the student must repeat the entire course the next academic year.

Specific conditions

Compulsory activities from previous semester may be approved by the department.

Required previous knowledge

Good understanding of basic linear algebra.

Course materials

The course material will be presented at the start of the course

More on the course
Facts

Version: 1
Credits:  7.5 SP
Study level: Second degree level

Coursework

Term no.: 1
Teaching semester:  SPRING 2022

Language of instruction: English

Location: Trondheim

Subject area(s)
  • Chemometrics
  • Signal Processing
  • Design of Experiments
  • Engineering Cybernetics
Contact information
Course coordinator: Lecturer(s):

Department with academic responsibility
Department of Engineering Cybernetics

Examination

Examination arrangement: Written examination

Term Status code Evaluation Weighting Examination aids Date Time Digital exam Room *
Spring ORD Written examination 100/100 D
Room Building Number of candidates
  • * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.
Examination

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

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