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 and time series 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.
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.
There will be a re-sit examination in both EBA 35301 and EBA 35302 autumn 2022 and last time spring 2023.
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: Written submission Weight: 40 Grouping: Individual Duration: 1 Semester(s) Comment: Written report consisting of 3-4 assignments given throughout the semester. Requires Python. Students will be given the opportunity to present and get feedback on their work during the semester and before submitting the report. Students are encouraged to support each other, but the assignments must be solved individually. Exam code: EBA 35303 Grading scale: ECTS Resit: Examination every semester |
Exam category: Submission Form of assessment: Written submission Invigilation Weight: 60 Grouping: Individual Support materials:
Duration: 3 Hour(s) Comment: Written school exam. 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 | 12 Hour(s) | |
Student's own work with learning resources | 127 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.