EBA 3530 Machine Learning and Forecasting
EBA 3530 Machine Learning and Forecasting
The course was last taught in spring 2024. Due to implementation of Revised Bachelor Model RBM, there are changes to the study plan, which means that the course will not be taught until spring 2026. However, re-sit exams will be offered in autumn 2024, spring 2025 and autumn 2025.
This course provides a thorough introduction to statistical, machine learning and forecasting techniques. The objective of this course is to present important statistical and machine learning methodologies that can be used to predict or classify outcomes.
In this course, the student will:
- Learn key statistical and machine learning methods used for prediction and classification.
- Get a thorough introduction to various forecasting techniques and evaluation.
- Understand the main difference between statistical and machine learning models.
After finishing this course, the student will be able to:
- Work with cross-sectional and time series data.
- Apply and choose among fundamental forecasting and machine learning methods.
- Choose between a statistical or machine learning model for classification or forecasting, depending on the problem at hand.
The students should be able to think critically about, and apply, statistical or machine learning techniques for forecasting and classification. A successful candidate will be in a good position to conduct applied data science work, or expand his/her knowledge in more advanced courses on the topic.
- Fundamental principles of statistical learning and forecasting techniques: bias/variance trade-off, cross validation techniques and pseudo out of sample methods.
- Key machine learning algorithms, including, regression, time series processes, regularization, and classification.
- The perceptron model and the principles of artificial neural networks, such as the multilayer perceptron model.
Lectures and practical exercises that require programming on a computer. Python will be used in applied work.
Please note that while attendance is not compulsory in all courses, it is the student’s own responsibility to obtain any information provided in class.
Higher Education Entrance Qualification
Disclaimer
Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.
EBA3500 Data Analysis with Programming, EBA 2904 Statistics, EBA 1180 Mathematics for Data Science, EBA 3400 Programming, data extraction and visualisation or equivalent courses.
| Assessments |
|---|
Exam category: Submission Form of assessment: Submission PDF Exam/hand-in semester: First Semester Weight: 40 Grouping: Group (2 - 3) Duration: 1 Semester(s) Exam code: EBA 35303 Grading scale: ECTS Resit: Examination every semester |
Exam category: School Exam Form of assessment: Written School Exam - pen and paper Exam/hand-in semester: First Semester Weight: 60 Grouping: Individual Support materials:
Duration: 3 Hour(s) Comment: Written school exam. All exams must be passed to obtain a final grade in the course. Exam code: EBA 35304 Grading scale: ECTS Resit: Examination every semester |
All exams must be passed to get a grade in this course.
| Activity | Duration | Comment |
|---|---|---|
Teaching | 36 Hour(s) | |
Seminar groups | 9 Hour(s) | |
Student's own work with learning resources | 130 Hour(s) | |
Submission(s) | 25 Hour(s) |
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 7,5 ECTS credit corresponds to a workload of at least 200 hours.
All exams must be passed to obtain a final grade in the course.