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2024/2025  KAN-CINTO2401U  Applied Machine Learning

English Title
Applied Machine Learning

Course information
Language English
Course ECTS 7.5 ECTS
Type Mandatory (also offered as elective)
Level Full Degree Master
Duration One Semester
Start time of the course Autumn
Timetable Course schedule will be posted at calendar.cbs.dk
Study board
Study Board for BSc/MSc in Business Administration and Information Systems, MSc
Course coordinator
  • Jason Burton - Department of Digitalisation (DIGI)
Main academic disciplines
  • Information technology
  • Statistics and quantitative methods
Teaching methods
  • Face-to-face teaching
Last updated on 23-01-2024

Relevant links
Learning objectives
  • Explain the real-world value of data and implement machine learning models to mobilize it for tasks like classification, regression, and clustering.
  • Evaluate methods for the testing and assessment of machine learning models and critically reflect on the meaning of findings.
  • Recognize the practical and ethical boundaries of machine learning.
  • Work collaboratively to create a valuable use case by identifying a relevant data set, applying appropriate machine learning models, and providing a deep evaluation of model performance and contextual considerations.
Course prerequisites
Students should have a basic understanding of statistics and a willingness to work with computational methods.
Examination
Applied Machine Learning:
Exam ECTS 7,5
Examination form Oral exam based on written product

In order to participate in the oral exam, the written product must be handed in before the oral exam; by the set deadline. The grade is based on an overall assessment of the written product and the individual oral performance, see also the rules about examination forms in the programme regulations.
Individual or group exam Oral group exam based on written group product
Number of people in the group 2-4
Size of written product Max. 15 pages
Assignment type Project
Release of assignment Subject chosen by students themselves, see guidelines if any
Duration
Written product to be submitted on specified date and time.
20 min. per student, including examiners' discussion of grade, and informing plus explaining the grade
Grading scale 7-point grading scale
Examiner(s) Internal examiner and second internal examiner
Exam period Autumn
Make-up exam/re-exam
Same examination form as the ordinary exam
Students can submit the same project or they can choose to submit a revised project. In the case of submitting a revised project, students must clearly indicate which parts of the project have been revised.
Course content, structure and pedagogical approach

This course is designed to equip students with foundational knowledge of machine learning and its application in business and society. Students will learn how to translate business questions into quantitative data-analytic tasks, study the principles and intuitions behind a variety of learning algorithms, and gain hands-on experience implementing machine learning models with Python. Importantly, students will also learn how to critically evaluate machine learning models for real-world relevance. This means not only being able to compare models with respect to predictive accuracy, but also to assess model fairness and perform appropriate mitigations, and to recognize when and why model outputs should not be interpreted in an explanatory way.

 

The course consists of weekly lectures, weekly exercise sessions, and a collaborative group project where students apply machine learning techniques to a topic and dataset of their own choosing.

 

Given the practical nature of this course, students with no prior programming experience are encouraged to complete a basic online tutorial to familiarize themselves with Python fundamentals (e.g., https://pandas.pydata.org/pandas-docs/version/0.15/10min.html). While the first two exercises provide a general introduction to programming with Python, the majority of the course is focused on implementing and evaluating machine learning models with libraries like 'pandas,' ‘scikit-learn,’ and ‘fairlearn.’
Description of the teaching methods
In-person lectures and in-person, hands-on exercise sessions with Python and Jupyter Notebooks.
Feedback during the teaching period
Students will receive feedback in three ways throughout the course. (1) The lecture sessions will incorporate anonymous polls whereby the students can test their understanding of concepts covered previously and then ask questions publicly. (2) During the exercise sessions the students will work in groups and receive peer-to-peer feedback, and also have the opportunity to receive specialised feedback from the professor as they work to ensure understanding of the practical aspects of the course. (3) Finally, students will be given the option of submitting a brief project plan mid-way through the course for the professor to provide written comments on.
Student workload
Lectures 22 hours
Exercises 22 hours
Class Preparation 102 hours
Exam and Preparation for Exam 60 hours
Total 206 hours
Expected literature

The literature can be changed before the semester starts. Students are advised to find the final literature in the syllabus on Canvas before purchasing any material.

 

  • Provost, F., & Fawcett, T. (2013). Data Science for Business: What you need to know about data mining and data-analytic thinking. O'Reilly Media, Inc.
  • Müller, A. C., & Guido, S. (2016). Introduction to machine learning with Python: a guide for data scientists. O'Reilly Media, Inc.
  • Barocas, S., Hardt, M., & Narayanan, A. (2023). Fairness and machine learning: Limitations and opportunities. MIT Press.
  • Rohrer, J. M. (2018). Thinking clearly about correlations and causation: Graphical causal models for observational data. Advances in Methods and Practices in Psychological Science, 1(1), 27-42.
  • Hofman, J. M., Sharma, A., & Watts, D. J. (2017). Prediction and explanation in social systems. Science, 355(6324), 486-488.
Last updated on 23-01-2024