NDAK16003U Introduction to Data Science (IDS)
MSc Programme in IT and Cognition
MSc Programme in Bioinformatics
MSc Programme in Molecular Biomedicine
The amount and complexity of available data is steadily
increasing. To make use of this wealth of information, computing
systems are needed that turn data into knowledge. Machine learning
is about developing the required software that automatically
analyses data for making predictions, categorizations, and
recommendations. Machine learning algorithms are already an
integral part of today's computing systems - for example in
search engines, recommender systems, or biometrical applications.
Machine learning provides a set of tools that is widely
applicable for data analysis within a diverse set of problem
domains such as data mining, search engines, digital image and
signal analysis, natural language modeling, bioinformatics,
physics, economics, biology, etc.
The purpose of the course is to introduce non-Computer Science students to probabilistic data modeling and the most common techniques from statistical machine learning and data mining. The students will obtain a working knowledge of basic data modeling and data analysis using fundamantal machine learning techniques.
This course is relevant for students from, among others, the studies of Cognition and IT, Bioinformatics, Physics, Biology, Chemistry, Economics, and Psychology.
The course covers the following tentative topic list:
- Foundations of statistical learning, probability theory.
- Classification methods, such as: Linear models, K-Nearest Neighbor.
- Regression methods, such as: Linear regression.
- Dimensionality reduction and visualization techniques such as principal component analysis (PCA).
At course completion, the successful student will have:
- the general principles of data analysis;
- elementary probability theory for modeling and analyzing data;
- the basic concepts underlying classification, regression, and clustering;
- common pitfalls in machine learning.
- applying linear and non-linear techniques for classification and regression;
- elementary data clustering;
- visualizing and evaluating results obtained with machine learning techniques;
- identifying and handling common pitfalls in machine learning;
- using machine learning and data mining toolboxes.
- recognizing and describing possible applications of machine learning and data analysis in their field of science;
- comparing, appraising and selecting machine learning methods for specific tasks;
- solving real-world data mining and pattern recognition problems by using machine learning techniques.
See Absalon when the course is set up.
- Practical exercises
- Theory exercises
PhD’s can register for MSc-course by following the same procedure as credit-students, see link above.
- 7,5 ECTS
- Type of assessment
- Continuous assessmentAssessment of 6-8 assignments weighted equally. Passed assignments cannot be transferred to another block.
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Several internal examiners.
20 minutes oral exam without preparation in course curriculum. No aids allowed.
Criteria for exam assesment
See Learning Outcome.