EBA 3530 Causality, Machine Learning and Forecasting
EBA 3530 Causality, Machine Learning and Forecasting
This course provides a thorough introduction to two central problems in applied analytics: causal analysis on one side, and machine learning and forecasting techniques on the other. The aims of the two problems are complementary, and are here presented together to emphasize their differences and connections.
In this course, the student will:
- Learn key Machine Learning algorithms used for prediction and classification.
- Get a thorough introduction to various forecasting techniques and evaluation.
- Understand the fundamental limitations of observational studies, and the types of assumptions that are required to use observational data to make causal (counter factual) statements.
- Understand how experiments and quasi-experiments can be used to overcome these difficulties.
After finishing this course, the student will be able to:
- Work with cross-sectional data, time series data, and text as data.
- Apply and choose among fundamental forecasting and Machine Learning methods.
- Apply regression techniques to analyze data from experiments and quasi-experiments.
The students should be able to think critically about, and apply, Machine Learning techniques for forecasting and causal inference. 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.
- The problems surrounding analysing causality through observational data.
- Experiments and quasi-experiments.
- Key Machine Learning algorithms, including, regression, time series processes, regularization, and classification, and their connection to the above issues.
Lectures and practical exercises that must be solved on the computer. Applied work will use Python/R.
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
Covid-19
Due to the Covid-19 pandemic, there may be deviations in teaching and learning activities as well as exams, compared with what is described in this course description.
Teaching
Information about what is taught on campus and other digital forms will be presented with the lecture plan before the start of the course each semester.
EBA3500 Data Analysis with Programming, EBA 2904 Statistics, EBA 2910 Mathematics for Business Analytics, EBA 3400 Programming, data extraction and visualisation or equivalent courses.
| Assessments |
|---|
Exam category: Submission Form of assessment: Written submission Weight: 25 Grouping: Group (2 - 3) Duration: 1 Week(s) Exam code: EBA 35301 Grading scale: ECTS Resit: Examination every semester |
Exam category: Submission Form of assessment: Written submission Invigilation Weight: 75 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 35302 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) | |
Group work / Assignments | 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 exam elements must be passed to achieve a final grade in the course.