GRA 4136 Predictive Analytics with Machine Learning

GRA 4136 Predictive Analytics with Machine Learning

Course code: 
GRA 4136
Department: 
Strategy and Entrepreneurship
Credits: 
6
Course coordinator: 
John Chandler Johnson
Course name in Norwegian: 
Predictive Analytics with Machine Learning
Product category: 
Master
Portfolio: 
MSc in Business Analytics
Semester: 
2021 Spring
Active status: 
Active
Level of study: 
Master
Teaching language: 
English
Course type: 
One semester
Introduction

This course teaches students how to use machine learning techniques and tools for predictive analytics. The course will also teach students to: specify problems accessible with these methods, evaluate analytics models, use new tools in the rapidly evolving automated machine learning field, and deploy machine learning models. The course assumes prior experience with: basic probability theory, traditional statistics for categorical and continuous independent variables, introductory Python programming, and MS Excel.

Learning outcomes - Knowledge

Students will learn about machine learning algorithms, translating business problems into problems machines can solve, and evaluating machine learning models. Students will also learn about recent advances in automated machine learning, and the implications for generalized deployment of the technology. Students will extend their programming expertise to machine learning applications, using predictive analytics to address business problems and to evaluate strategic options. Students will become familiar with automated machine learning tools. Because these automated ML tools are very new and changing very rapidly, final decisions on tools will depend on the “state of the art” when the course starts.

Learning outcomes - Skills

Students completing the course will be able to: specify business problems such that predictive analytics can be applied; evaluate and specify project data requirements; execute predictive analytics on a broad range of problems; evaluate predictive analytics model results; and discuss results/requirements both with data scientists and with practitioners.

General Competence

Positioning data vis-à-vis methods, as in predictive analytics, is a natural opportunity to inculcate a perspective that organizations are problem-solving, data-driven entities. Predictive analytics offers a framework in which students can reflect on the value of data for a particular problem, or the value of a method given particular data. The course will encourage students to reflect on capturing and creating data-driven value.

Course content

This course focuses on predictive analytics using machine learning: supervised learning (classification and regression). The course will also introduce unsupervised learning, including clustering and dimensionality reduction. Students will implement and tune individual machine learning algorithms (e.g., via Python’s scikit-learn library), and work with automated machine learning tools. In addition to lectures, the course will include cases and in-class workshops.

Teaching and learning activities

The course will use a variety of software, including: Continuum's Anaconda software suite, DataRobot, and MS Excel. This field is changing very rapidly and new tools are released monthly. To ensure that the course stays current, the instrutor may introduce new software. Such changes will not require any prerequisite changes or fundamentally alter the nature of the course. 

During class, BI will provide the students with a 9-month academic license for DataRobot. BI will pay the license fee.

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.

 

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.

This is a course with continuous assessment (several exam components) and one final exam code. Each exam component is graded by using points on a scale from 0-100. The components will be weighted together according to the information in the course description in order to calculate the final letter grade for the examination code (course). Students who fail to participate in one/some/all exam elements will get a lower grade or may fail the course. You will find detailed information about the point system and the cut off points with reference to the letter grades when the course start.

At re-sit all exam components must, as a main rule, be retaken during next scheduled course.

Qualifications

All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have specific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.

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.

Required prerequisite knowledge

The course assumes prior experience with: basic probability theory, traditional methods for analysis of continuous and categorical variables, and introductory Python programming.

Exam categoryWeightInvigilationDurationGroupingComment exam
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA 41363
Grading scale:
Point scale
Grading rules:
Internal and external examiner
Resit:
All components must, as a main rule, be retaken during next scheduled course
40No72 Hour(s)Individual Machine learning take-home exam.
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA 41363
Grading scale:
Point scale
Grading rules:
Internal examiner
Resit:
All components must, as a main rule, be retaken during next scheduled course
20No1 Week(s)Group ( 3 - 4)The students will be assigned to groups.
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA 41363
Grading scale:
Point scale
Grading rules:
Internal examiner
Resit:
All components must, as a main rule, be retaken during next scheduled course
20No1 Week(s)Group (3 - 4)The students will be assigned to groups.
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA 41363
Grading scale:
Point scale
Grading rules:
Internal examiner
Resit:
All components must, as a main rule, be retaken during next scheduled course
20No1 Week(s)Group (3 - 4)The students will be assigned to groups.
Exams:
Exam category:Submission
Form of assessment:Written submission
Weight:40
Invigilation:No
Grouping (size):Individual
Duration:72 Hour(s)
Comment:Machine learning take-home exam.
Exam code:GRA 41363
Grading scale:Point scale
Resit:All components must, as a main rule, be retaken during next scheduled course
Exam category:Submission
Form of assessment:Written submission
Weight:20
Invigilation:No
Grouping (size):Group (3-4)
Duration:1 Week(s)
Comment:The students will be assigned to groups.
Exam code:GRA 41363
Grading scale:Point scale
Resit:All components must, as a main rule, be retaken during next scheduled course
Exam category:Submission
Form of assessment:Written submission
Weight:20
Invigilation:No
Grouping (size):Group (3-4)
Duration:1 Week(s)
Comment:The students will be assigned to groups.
Exam code:GRA 41363
Grading scale:Point scale
Resit:All components must, as a main rule, be retaken during next scheduled course
Exam category:Submission
Form of assessment:Written submission
Weight:20
Invigilation:No
Grouping (size):Group (3-4)
Duration:1 Week(s)
Comment:The students will be assigned to groups.
Exam code: GRA 41363
Grading scale:Point scale
Resit:All components must, as a main rule, be retaken during next scheduled course
Type of Assessment: 
Continuous assessment
Total weight: 
100
Student workload
ActivityDurationComment
Teaching
36 Hour(s)
Examination
24 Hour(s)
Prepare for teaching
36 Hour(s)
Group work / Assignments
40 Hour(s)
Student's own work with learning resources
24 Hour(s)
Sum workload: 
160

A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.