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2019/2020  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 60
Study board
Study Board for MSc in Economics and Business Administration
Course coordinator
  • Course instructor: Dr. Bowei Chen, Assistant Professor, Adam Smith Business School, University of Glasgow, UK.
    Sven Bislev - Department of Management, Society and Communication (MSC)
For academic questions related to the course, please contact instructor Bowei Chen at Bowei.Chen@glasgow.ac.uk
Other academic questions: contact academic director Sven Bislev at sb.msc@cbs.dk
Main academic disciplines
  • Finance
  • Information technology
  • Economics
Teaching methods
  • Face-to-face teaching
Last updated on 12/11/2019

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 implement machine learning algorithms to solve business problems
Course prerequisites
Fundamental mathematics of machine learning will be reviewed in the course. However, students are expected to have received some mathematics training in their undergraduate studies, e.g., calculus, linear algebra and probability theory. No programming skills and experience are needed. Students will learn Python for machine learning from scratch in this course.
Examination
Machine Learning for Predictive Analytics in Business:
Exam ECTS 7.5
Examination form Written sit-in exam on CBS' computers
Individual or group exam Individual exam
Assignment type Written assignment
Duration 4 hours
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Summer, Ordinary exam: 4 hour written exam in the period of 27–31 July 2020
Retake exam: 4 hour written exams in the period of 28 September–2 October 2020
3rd attempt (2nd retake) exam: 72-hour home assignment- 23-26 November 2020 – for all ISUP courses simultaneously

Exam schedules available on https:/​/​www.cbs.dk/​uddannelse/​international-summer-university-programme-isup/​courses-and-exams
Aids Limited aids, see the list below:
The student is allowed to bring
  • Any calculator
  • Language dictionaries in paper format
The student will have access to
  • Advanced IT application package
Make-up exam/re-exam
Same examination form as the ordinary exam
If the number of registered candidates for the make-up examination/re-take examination warrants that it may most appropriately be held as an oral examination, the programme office will inform the students that the make-up examination/re-take examination will be held as an oral examination instead.
Retake exam: 4 hour written sit-in exam, 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 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 of machine learning will be reviewed, and students will learn Python for predictive analytics from scratch in this course.

 

Each class will be held in the classroom. It is combined with lecture (theory) and workshop (practice). Students will need to bring their own laptops to the classroom (connected to Wifi) or the teaching can be delivered in the computer lab.  
 
Preliminary Assignment: A couple of questions related to the fundamental mathematics of machine learning
 
Class 1: Introduction to Machine Learning and Its Applications in Business
Class 2: Review of Business Mathematics and Statistics
Class 3: Introduction to Python Programming for Business Analytics
Class 4: Linear Regression  
Class 5: Logistic Regression
Class 6: Artificial Neural Networks 
 
Midway Mock Exam
 
Class 7: Naive Bayes and K-Nearest Neighborhood Methods
Class 8: Tree-Based Models 
Class 9: Support-Vector Machines 
Class 10: Cluster Analysis
Class 11: Review Session and Q&A for the Exam
Description of the teaching methods
All teaching takes place on campus (notice that face-to-face teaching may include the use of online materials and tools.
Feedback during the teaching period
Student survey feedback.
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 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 end March 2020.
Expected literature

Mandatory readings:

 

Kevin Murphy. Machine Learning: A Probabilistic Perspective, MIT Press 2012.
 
Christopher Bishop. Pattern Recognition and Machine Learning, Springer 2007.

 

 

Additional relevant readings:

 

Mohammed Zaki and Wagner Meira. Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge University Press, 2014

Last updated on 12/11/2019