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2023/2024  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
Min. participants 30
Max. participants 60
Study board
Study Board for cand.merc. and GMA (CM)
Course coordinator
  • Raghava Rao Mukkamala - Department of Digitalisation (DIGI)
For academic questions related to the course, please contact course responsible Raghava Rao Mukkamala (rrm.digi@cbs.dk).
Main academic disciplines
  • Finance
  • Information technology
  • Economics
Teaching methods
  • Face-to-face teaching
Last updated on 22/11/2023

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
Completed Bachelor degree or equivalent. 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.
Examination
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 Max. 10 pages
Assignment type Written assignment
Release of assignment The Assignment is released in Digital Exam (DE) at exam start
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
Make-up exam/re-exam
Same examination form as the ordinary exam
First re-take: 72-hour home assignment, max. 10 pages. 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.
Description of the exam procedure

Home assignment written in parallel with the course.
Course content, structure and pedagogical approach

Machine learning has gained widespread recognition for its significant contributions to various facets of business operations, including tasks such as fraud detection, sales forecasting, inventory pricing, and consumer segmentation. This course is tailored to introduce business students to the world of machine learning. It places a strong emphasis on predictive analytics, empowering students to address business challenges using data-driven machine learning algorithms. The course is comprehensive, offering a balanced blend of theory and practical application. It covers essential mathematical and statistical concepts and guides students in mastering Python programming from scratch. Each class comprises both theoretical lectures and practical workshops. To ensure active participation, students are required to bring their own laptops to class.

   

Preliminary assignment: Several questions and tasks related to mathematical fundamentals and the installation of Python.

Class 1: Introduction and getting started with Python 

Class 2: Data manipulation using Python

Class 3: Data visualization in Python

Class 4: Linear regression

Class 5: Logistic regression

Class 6: Artificial neural networks

Feedback activity: A small assignment (with several questions)

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
The teaching methods involve face-to-face instruction in the classroom, and students are advised to bring their laptops for the purpose of conducting practical analytics exercises.
Feedback during the teaching period
Student survey feedback.
Student workload
Preliminary assignment 20 hours
Classroom attendance 30 hours
Preparation 129 hours
Feedback activity 7 hours
Examination 20 hours
Further Information
6-week course.
 
Preliminary Assignment: The course coordinator uploads Preliminary Assignment on Canvas at the end of May. It is expected that students participate as it will be included in the final exam, but the assignment is without independent assessment and grading.
 

 
Expected literature

Recommended textbooks:

  • 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 22/11/2023