ELE 3912 Machine Learning for Business Using R

ELE 3912 Machine Learning for Business Using R

Course code: 
ELE 3912
Department: 
Data Science and Analytics
Credits: 
7.5
Course coordinator: 
Emil Aas Stoltenberg
Course name in Norwegian: 
Machine Learning for Business Using R
Product category: 
Bachelor
Portfolio: 
Bachelor - Programme Electives
Semester: 
2024 Autumn
Active status: 
Active
Level of study: 
Bachelor
Teaching language: 
English
Course type: 
One semester
Introduction

In finance, marketing, consulting, public policy, science, you name it, people who can make sense of large and complex data sets are in high demand. Machine learning refers to a vast set of tools that helps us in making sense of data. This course gives an introduction to various machine learning methods, concentrating on those that fall under the umbrella of supervised learning. The course contains both model based and algorithm based machine learning approaches, contrasting the advantages and limitations of the two. The course contains some mathematical statistics meant to enable the students to peek into various algorithms. We will use the programming language R.

Learning outcomes - Knowledge

During the course students shall learn:

  • To think critically about the possibilities and limitations of machine learning.
  • When to use algorithm based approaches to data analysis, and when a model based approach might be called for.
  • To understand the differences between predicting and explaining phenomena, and how this relates to choice of methodology.
  • The importance of uncertainty quantification.
  • How to identify challenges related to big data.
Learning outcomes - Skills

After completed course students shall be able to:

  • Implement a large set of machine learning methods in R.
  • Be able to evaluate and compare the performance of different methods.
  • Produce graphics illustrating one's findings and predictions, as well as the performance of ones methods.
General Competence

Students will understand that in many situations machine learning tools are a prerequisite for decision making, but also be critically aware of its limitations and underlying assumptions.

Course content
  • Introduction to R.
  • What is machine learning.
  • Linear regression, including dimension reduction methods.
  • Classification. Logistic regression, linear discriminant analysis, naive Bayes, and K nearest neighbours.
  • Bootstrapping and other resampling methods.
  • Tree-based methods. Bagging, random forests, boosting.
  • Data cleaning.
Teaching and learning activities

The course consists of a combination of lectures and problemsolving in class using R. 

The students are expected to do all the homework, though it is not mandatory.

Software tools
R
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

EBA 2904 Statistics with Programming or equivalent.

Basic mathematics at bachelor's level

Assessments
Assessments
Exam category: 
Submission
Form of assessment: 
Submission PDF
Exam/hand-in semester: 
First Semester
Weight: 
40
Grouping: 
Group/Individual (1 - 3)
Duration: 
2 Week(s)
Comment: 
A 2 week take home exam, concentrating of the analysis of one or more data sets. Will also contain some theory. All exams must be passed to obtain a final grade in the course.
Exam code: 
ELE 39123
Grading scale: 
ECTS
Resit: 
Examination when next scheduled course
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
  • Bilingual dictionary, Native tongue - English
Duration: 
3 Hour(s)
Comment: 
Regular school exam. All exams must be passed to obtain a final grade in the course.
Exam code: 
ELE 39124
Grading scale: 
ECTS
Resit: 
Examination when next scheduled course
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)
Feedback activities and counselling
36 Hour(s)
Homework sessions, problemsolving under supervision, etc.
Student's own work with learning resources
45 Hour(s)
Submission(s)
30 Hour(s)
Examination
53 Hour(s)
A 2 week take home exam and final exam.
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.