Course description for 2023/24
Statistical and Machine Learning
FIN5002
Course description for 2023/24
Statistical and Machine Learning
FIN5002
The course provides an introduction to machine learning from a statistical point of view. It provides an overview of new processes and technology, as well as training in specific methods and tools that can be used to analyze large amounts of data, as well as how results can best be communicated to different stakeholders. The course provides a thorough introduction to how to retrieve and process different types of data, as well as statistical methods for analyzing the amount of data in the supervised and unsupervised setting of machine learning. Methods reviewed in the regression and classification context include amongst others regression, decision trees and neural networks. Such methods are best learned through practice, so the course comprises a practical part with the statistical software R for retrieving data, analyzing the amount of data, and communicating and visualizing the results.
Knowledge
Students:
- Has in-depth knowledge of areas within data analysis and statistical methods for conducting an analysis of data sets of different types and sizes.
- Has in-depth knowledge of international research in topics related to the course, such as regression analysis, decision trees and machine learning.
- Have knowledge and practical understanding of data integrity, including reliability of data, complete and accurate data transfer, data washing and data clearing.
- Can apply knowledge in new areas within data analysis, map the data flow in systems and processes that are relevant to different industries.
- Can analyze academic issues with tools and techniques from the subject's uniqueness.
Skills
Students:
- Can analyze existing models, theories and methods in statistics for large amounts of data using appropriate tools
- Can use relevant methods and theories for research within the subject's main topics in an independent way.
- Can analyze and critically relate to different sources of information used in different contexts within automation of work tasks.
- Can carry out, produce and evaluate an independent analytical research work under supervision in line with research ethics norms.
- Can use analytical and digital tools, such as R, in practical work, both for obtaining data sets, analysis of data sets, as well as communication of results and analyzes both written, oral and graphical
General competence
Students:
- Has a thorough understanding of the change processes that are taking place, and is expected in the future, as well as analyzing relevant issues within digitization of various industries.
- Can convey extensive independent work and masters forms of expression in digitization, automation and the relevance of future work tasks, such as presentation of graphic illustration of connections between variables and changes over time, individuals or both
- Can apply his expertise in new areas and issues within business analysis and consulting.
- Can contribute to new thinking and innovative solutions to problems related to digitization and automation.
- Can discuss and communicate professional issues with different stakeholders.
- Can assess and consider the most appropriate method of digital business analysis for a given industry or data set.
- Can apply and easily acquire skills in new digital tools.
Paid semester fee and syllabus literature. It is also required that students have a laptop at their disposal.
Elective course
Weekly lectures
The study programme is evaluated annually by students by way of course evaluation studies. These evaluations are included in the universitys quality assurance system.
Composite assessment, grading rule Letter grades
- Written school exam, 4 hours, counts 60/100 of the grade, grading rule Letter grades.
- Assignment - group work, counts 20/100 of the grade, grading rule Letter grades.
- Oral - Presentation, counts 20/100 of the grade, grading rule Letter grades.
We reserve the right to change the assessment method. The correct assessment method is displayed in StudentWeb when registering for the relevant course.
Bilingual dictionary and simple calculator.
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