course-details-portlet

IT3212 - Data Powered Software

About

Examination arrangement

Examination arrangement: Portfolio
Grade: Letter grades

Evaluation Weighting Duration Grade deviation Examination aids
Portfolio 100/100

Course content

This course will provide the following:

  1. Handle real world data produced from computer applications, such as user interfaces, services, and other programs.
  2. Perform adequate data processes (e.g., feature extraction, selection and dimensionality reduction) to support software development.
  3. Employ ML techniques (e.g., supervised, unsupervised, semi- and weak-supervised) to enrich contemporary software development.
  4. Learn to Solve real-world problems by using software with data-powered modelling techniques (e.g., knowledge inference).

More concretely, we will cover the following topics:

  1. Introduction to data and basic statistics.
  2. Computer data pre-processing and calibration (e.g., data processing and signal processing techniques)
  3. Knowledge extraction techniques from computer data (e.g., feature extraction, feature space reduction).
  4. Basic modeling techniques (e.g., KNN, Naive Bayes, Gaussian process, Decision boundaries)
  5. Advanced modeling techniques (e.g., SVM, random forest, ANN, and ensemble learning algorithms)
  6. Empowering systems with unsupervised learning capabilities (e.g., personalization services and recommended systems).
  7. Empowering systems with semi-supervised and weak-supervised learning capabilities (e.g., adaptive support).
  8. Empowering systems with real-time capabilities (e.g., through time series analysis and forecasting)
  9. Putting things together: Utilizing data-powered techniques to design and develop contemporary software.

Learning outcome

  • Knowledge
  1. Analysing real world data with statistics and machine learning
  2. Feature selection and dimensionality reduction techniques
  3. Application of supervised, unsupervised, semi- and weak-supervised learning
  • Skill
  1. Translating real-world problems to machine learning space
  2. Use of appropriate pipeline (series of methods)
  • Competence
  1. Know the field of machine learning as seen from SE, IS, HCI industries

Learning methods and activities

Lectures and a project. Each team has to submit project deliverables during the semester and a final report. Grading is team-based, but individual grades can be given in special cases. The course will be held in English.

Further on evaluation

Three assignments are the basis for the grade in the course. The students have to submit the assignments throughout the semester.

If a student wants to retake the course the whole course need to be done.

Required previous knowledge

The course is for all MSIT, MTDT, MIDT, BIT and MLREAL math and informatics profiles.

Course materials

Will be given at the start of the semester.

More on the course

No

Facts

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

Coursework

Term no.: 1
Teaching semester:  AUTUMN 2024

Language of instruction: English

Location: Trondheim

Subject area(s)
  • Computer Science
  • Engineering Subjects
Contact information
Course coordinator:

Department with academic responsibility
Department of Computer Science

Timetable

List view
Calendar view

Examination

Examination arrangement: Portfolio

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Autumn ORD Portfolio 100/100

Submission
2024-11-25


14:00

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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