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2020/2021  KAN-CCMVI2085U  Machine Learning for Predictive Analytics in Business

English Title
Machine Learning for Predictive Analytics in Business

Course information

Language English
Course ECTS 7.5 ECTS
Type Elective
Level Full Degree Master
Duration Summer
Start time of the course Summer
Timetable Course schedule will be posted at calendar.cbs.dk
Max. participants 80
Study board
Study Board for MSc in Economics and Business Administration
Course coordinator
  • Bowei Chen - Department of International Economics, Goverment and Business (EGB)
For academic questions related to the course, please contact instructor Bowei Chen at bch.egb@cbs.dk
Main academic disciplines
  • Finance
  • Information technology
  • Economics
Teaching methods
  • Online teaching
Last updated on 27/04/2021

Relevant links

Learning objectives
By the end of this course students will be able to:
  • Have a good understanding of the fundamental issues and challenges of machine learning
  • Appropriately choose and appraise machine learning algorithms for predictive analytics in business
  • Effectively use Python to process, summarize and visualize business data
  • Effectively use Python to develop machine learning algorithms to solve business problems
Course prerequisites
Fundamental mathematics and statistics will be reviewed in the course. However, students are expected to have received some mathematics and/or statistics training in their undergraduate studies, e.g., calculus, linear algebra and probability theory. No programming skills and experience are needed. Students will learn Python programming from scratch in this course.
Machine Learning for Predictive Analytics in Business:
Exam ECTS 7.5
Examination form Home assignment - written product
Individual or group exam Individual exam
Size of written product Please see text below
4 hour home assignment. No requirement for maximum number of pages.
Assignment type Written assignment
Duration Written product to be submitted on specified date and time.
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Summer, Ordinary exam: 4-hour home assignment in the period of 26–30 July 2021
Retake exam: 4-hour home assignment in the period of 20 – 24 September 2021
3rd attempt (2nd retake) exam: 72-hour home assignment – 22 – 25 November 2021 – for all ISUP courses simultaneously
Exam schedules available on https:/​/​www.cbs.dk/​uddannelse/​international-summer-university-programme-isup/​courses-and-exams
Make-up exam/re-exam
Same examination form as the ordinary exam
Retake exam: 4 hour home assignment, new exam question
Exam form for 3rd attempt (2nd retake): 72-hour home project assignment, max. 10 pages.
Course content, structure and pedagogical approach

Machine learning has been widely applied to support business in a range of areas, such as fraud detection, sales forecasting, inventory pricing, and consumer segmentation. This course introduces machine learning to students in business subjects. There will be a strong emphasis on predictive analytics, to enable students to frame and solve business problems using data-driven machine learning algorithms. It is self-contained, covers both theory and practice. Fundamental mathematics and statistics will be reviewed, and students will learn Python programming from scratch in this course. Each class is combined with lecture (theory) and workshop (practice). Students need to have their computers with Internet access. 


Preliminary assignment: A couple of questions related to business mathematics and statistics

Class 1: Introduction and review of mathematics and statistics

Class 2: Basic Python syntax, data I/O and handling

Class 3: Data visualization in Python

Class 4: Linear models for regression  

Class 5: Logistic regression

Class 6: Artificial neural networks

Midway mock exam

Class 7: K-nearest neighbors and naive Bayes 

Class 8: Tree-based models

Class 9: Support-vector machines 

Class 10: Cluster analysis

Class 11: Course review and Q&A for the exam

Description of the teaching methods
This year all courses are taught digitally over the Internet. Instructors will apply direct/live teaching through a link (like Skype, Team, Zoom). In some courses, pre-recorded material will also be used.
Feedback during the teaching period
Student survey feedback.
Mock exam solutions.
Student workload
Preliminary assignment 20 hours
Classroom attendance 33 hours
Preparation 126 hours
Feedback activity 7 hours
Examination 20 hours
Further Information
Preliminary assignment: To help students get maximum value from ISUP courses, instructors provide a reading or a small number of readings or video clips to be read or viewed before the start of classes with a related task scheduled for class 1 in order to 'jump-start' the learning process.

Course timetable is/will be available on https://www.cbs.dk/uddannelse/international-summer-university-programme-isup/courses-and-exams
We reserve the right to cancel the course if we do not get enough applications. This will be communicated on https://www.cbs.dk/uddannelse/international-summer-university-programme-isup/courses-and-exams in March 2021.
Expected literature

Mandatory readings:

  • Kevin Murphy. Machine Learning: A Probabilistic Perspective, MIT Press 2012. (Chapters 1-3, 7, 8, 14, 16)
  • Christopher Bishop. Pattern Recognition and Machine Learning, Springer 2007. (Chapters 5, 9)
  • Wes McKinney. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, O'Reilly, 2012. (Chapters 4, 5)

Additional relevant readings:

  • Marc Deisenroth, Aldo Faisal, and Cheng Soon Ong. Mathematics for Machine Learning, Cambridge University Press, 2020. (Chapters 2, 4-6)
Last updated on 27/04/2021