Data Mining and Deep Learning (DAT550)

The purpose of this course is for students to gain knowledge and practical experience of data mining and deep learning techniques. The course will prepare the students with a deep knowledge of technologies and be able to prepare large-scale data for data mining (pre-processing), feature extraction, dimensionality reduction and use a number of supervised and unsupervised methods for classification, regression and clustering tasks that can help to extract actionable knowledge. The course will provide the opportunity for students to learn state-of-the-art data mining and deep learning algorithms and tools. The students will get hands-on experience to try these tools on real data through lab assignments and a project.

Course description for study year 2024-2025. Please note that changes may occur.


Course code




Credits (ECTS)


Semester tution start


Number of semesters


Exam semester


Language of instruction



  • Unsupervised learning

    • Data cleansing, transformation and preparation
    • Clustering
    • Dimensionality reduction, SVD, PCA
  • Supervised learning

    • Classification
    • Neural Networks and Deep learning
    • Recommendation systems

Learning outcome



  • has advanced knowledge in the field and specialized in the theory and practice of data preparation, selection and mining.
  • has in-depth knowledge of the scientific or art theory of the subject area and methods to gain insight from large data collections.
  • can apply knowledge in new areas of data mining and deep learning
  • can analyze knowledge extraction issues on the basis of data mining and deep learning


Candidate can:

  • Be able to analyze and critically relate to various sources of information and use these to structure and formulate professional reasoning for various data mining tasks
  • Be able to analyze existing theories, methods and interpretations within the area of data mining and deep learning and work independently with problem-solving on data mining and deep learning tasks
  • Be able to use relevant data mining methods such as clustering, classification, graph, stream mining, frequent pattern mining, association rule mining, deep learning for research and professional application development
  • Be able to carry out an independent, limited research or development project under supervision and in accordance with current research ethics standards which involves preparing data mining pipelines, evaluation, and tune parameters for various data mining models and deep learning using state-of-the-art tools.

General competencies


  • can analyze relevant professional, research and ethical issues in data mining and deep learning
  • can apply their knowledge and skills in new areas to carry out advanced tasks and projects
  • can provide extensive independent work on data mining and deep learning issues
  • can communicate about data mining and deep learning issues, analyzes and conclusions within the subject area, both with specialists and to the general public.
  • can contribute to research and innovation in data mining and deep learning.
  • identify the theoretical and practical issues behind various data mining and deep learning techniques. Being able to list and describe strengths, limitations and trade-offs among various data mining techniques and choose the appropriate techniques for solving data science problems for various applications.

Required prerequisite knowledge


Recommended prerequisites

DAT120 Introduction to Programming, STA500 Probability and Statistics 2


Written exam and project report

Form of assessment Weight Duration Marks Aid
Written exam 3/5 4 Hours Letter grades - 1)
Project report 2/5 Letter grades

1) Textbooks and Lecture notes

Three obligatory assignments are graded, amounting to 3/20 (15%) of the final grade.Project consisting one large assignment. The project is to be performed in a group. The grade for the project will be based on the submitted program code, project report document and an oral hearing in groups of the submitted program code and report. Both parts must be done before final grade is given. If a student fails the project, she/he has to take this part next time the subject is lectured.The written exam will be digital (Inspera).Both exam units must be passed in order to receive a final grade in the course.

Coursework requirements

Mandatory assignments

Three mandatory graded (A-F) exercises/programming assignments amounting to 3/20 (15%) of the final grade.

All programming exercises must be passed to attend for the written exam and to get project approved. Completion of mandatory lab assignments are to be made on time. Absence due to illness or for other reasons must be communicated as soon as possible to the laboratory personnel. One cannot expect that provisions for completion of the lab assignments at other times are made unless prior arrangements with the laboratory personnel have been agreed upon. Failure to complete the assigned labs on time or not having them approved will result in barring from taking the exam of the course.

Course teacher(s)

Course coordinator:

Vinay Jayarama Setty

Course teacher:

Mina Farmanbar

Head of Department:

Tom Ryen

Method of work

4 hours lectures/exercises and 2 hours of guided programming exercises and project. Programming exercises requires additional non-guided work effort.

Overlapping courses

Course Reduction (SP)
Web Search and Data Mining (DAT630_1) 5

Open for

Admission to Single Courses at the Faculty of Science and Technology
Computer Science - Master of Science Degree Programme

Course assessment

There must be an early dialogue between the course supervisor, the student union representative and the students. The purpose is feedback from the students for changes and adjustments in the course for the current semester.In addition, a digital subject evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.


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