EBA 3530 Machine Learning and Forecasting

EBA 3530 Machine Learning and Forecasting

Course code: 
EBA 3530
Department: 
Data Science and Analytics
Credits: 
7.5
Course coordinator: 
Rogelio Andrade Mancisidor
Course name in Norwegian: 
Machine Learning and Forecasting
Product category: 
Bachelor
Portfolio: 
Bachelor of Data Science for Business - Programme Courses
Semester: 
2024 Autumn
Active status: 
Re-sit exam
Level of study: 
Bachelor
Resit exam semesters: 
2024 Autumn
2025 Spring
2025 Autumn
Resit exam info

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.

Teaching language: 
English
Course type: 
One semester
Introduction

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.

Learning outcomes - Knowledge

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.
Learning outcomes - Skills

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.
General Competence

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.

Course content
  • 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. 
Teaching and learning activities

Lectures and practical exercises that require programming on a computer. Python will be used in applied work.

Software tools
Software defined under the section "Teaching and learning activities".
Additional information

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.

 

Qualifications

Higher Education Entrance Qualification

Disclaimer

Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.

Required prerequisite knowledge

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
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)
Comment: 
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 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: 
  • BI-approved exam calculator
  • Simple calculator
  • Bilingual dictionary
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
Type of Assessment: 
Ordinary examination
All exams must be passed to get a grade in this course.
Total weight: 
100
Student workload
ActivityDurationComment
Teaching
36 Hour(s)
Seminar groups
9 Hour(s)
Student's own work with learning resources
130 Hour(s)
Submission(s)
25 Hour(s)
Sum workload: 
200

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.

Semester

Oversettelser