EBA 3530 Machine Learning and Forecasting
EBA 3530 Machine Learning and Forecasting
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.
Exam category | Weight | Invigilation | Duration | Support materials | Grouping | Comment exam |
---|---|---|---|---|---|---|
Exam category: Submission Form of assessment: Written submission Exam code: EBA 35303 Grading scale: ECTS Grading rules: Internal examiner Resit: Examination every semester | 40 | No | 1 Semester(s) | Group ( 2 - 3) | The written report will consist of 1-2 assignments, to be answered in groups of 2-3 students. These assignments will be published in itslearning/GitHub throughout the semester. Read each assignment carefully for detailed information about the length and the type of files to be uploaded, some of the assignments require Python coding. Prepare one document with your final solutions in PDF format, which must be uploaded into WiseFlow as the main answer paper. In case it is required, you can add attachments, e.g. Python code. Students will be given the opportunity to present and get feedback on their work during the semester and before submitting the assignments. All exams must be passed to obtain a final grade in the course. | |
Exam category: Submission Form of assessment: Written submission Exam code: EBA 35304 Grading scale: ECTS Grading rules: Internal examiner Resit: Examination every semester | 60 | Yes | 3 Hour(s) |
| Individual | Written school exam. All exams must be passed to obtain a final grade in the course. |
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.