Course - Data Powered Software - IT3212
IT3212 - Data Powered Software
About
New from the academic year 2023/2024
Examination arrangement
Examination arrangement: Report
Grade: Letter grades
Evaluation | Weighting | Duration | Grade deviation | Examination aids |
---|---|---|---|---|
Report | 100/100 |
Course content
This course will provide the following:
- Handle real world data produced from computer applications, such as user interfaces, services, and other programs.
- Perform adequate data processes (e.g., feature extraction, selection and dimensionality reduction) to support software development.
- Employ ML techniques (e.g., supervised, unsupervised, semi- and weak-supervised) to enrich contemporary software development.
- 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:
- Introduction to data and basic statistics.
- Computer data pre-processing and calibration (e.g., data processing and signal processing techniques)
- Knowledge extraction techniques from computer data (e.g., feature extraction, feature space reduction).
- Basic modeling techniques (e.g., KNN, Naive Bayes, Gaussian process, Decision boundaries)
- Advanced modeling techniques (e.g., SVM, random forest, ANN, and ensemble learning algorithms)
- Empowering systems with unsupervised learning capabilities (e.g., personalization services and recommended systems).
- Empowering systems with semi-supervised and weak-supervised learning capabilities (e.g., adaptive support).
- Empowering systems with real-time capabilities (e.g., through time series analysis and forecasting)
- Putting things together: Utilizing data-powered techniques to design and develop contemporary software.
Learning outcome
- Knowledge
- Analysing real world data with statistics and machine learning
- Feature selection and dimensionality reduction techniques
- Application of supervised, unsupervised, semi- and weak-supervised learning
- Skill
- Translating real-world problems to machine learning space
- Use of appropriate pipeline (series of methods)
- Competence
- 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.
Compulsory assignments
- Three assignments related to the final report
Further on evaluation
The report from the project is the basis for the grade in the course. The students also have to submit the mandatory deliverables throughout the semester. The mandatory activity are three different assignments related to the final report.
Retake of the course will require new deliverables/report.
Required previous knowledge
The course is for students MSIT profile "Interaksjonsdesign spill- og læringsteknologi", MTDT/MIDT profiles "Programvareutvikling", "Digital virksomhetsutvikling", or "Interaksjonsdesign og spillteknologi" or LUR students specialising in mathematics and informatics (or contact the teacher).
Course materials
Will be given at the start of the semester.
No
Version: 1
Credits:
7.5 SP
Study level: Second degree level
Term no.: 1
Teaching semester: AUTUMN 2023
Language of instruction: English
Location: Trondheim
- Computer Science
- Engineering Subjects
Department with academic responsibility
Department of Computer Science
Examination
Examination arrangement: Report
- Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
-
Autumn
ORD
Report
100/100
Submission
2023-12-10
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.
For more information regarding registration for examination and examination procedures, see "Innsida - Exams"