2024/2025 KAN-CINTO4003U Artificial Intelligence and Machine Learning
English Title | |
Artificial Intelligence and 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 | Spring |
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 | |
|
|
Main academic disciplines | |
|
|
Teaching methods | |
|
|
Last updated on 23-01-2024 |
Relevant links |
Learning objectives | ||||||||||||||||||||||||||
To achieve the grade 12, students should meet the
following learning objectives with no or only minor mistakes or
errors:
|
||||||||||||||||||||||||||
Course prerequisites | ||||||||||||||||||||||||||
Basic programming skills and basic knowledge of machine learning, including standard models for classification and regression | ||||||||||||||||||||||||||
Prerequisites for registering for the exam (activities during the teaching period) | ||||||||||||||||||||||||||
Number of compulsory
activities which must be approved (see section 13 of the Programme
Regulations): 2
Compulsory home
assignments
Each student has to get 2 out of 3 home assignments approved in order to participate in the ordinary exam. The assignments are written individually and are max. 5 pages each. There will not be any extra attempts provided to the students before the ordinary exam. If a student cannot participate due to documented illness, or if a student does not get the activities approved in spite of making a real attempt, then the student will be given one extra attempt before the re-exam. Before the re-exam, there will be one home assignment (10 pages) which will cover 2 mandatory assignments. |
||||||||||||||||||||||||||
Examination | ||||||||||||||||||||||||||
|
||||||||||||||||||||||||||
Course content, structure and pedagogical approach | ||||||||||||||||||||||||||
AI is poised to transform the business and technology landscape, and it has become essential for business leaders to understand the key technologies and concepts involved. This course covers several of the main AI technologies, including natural language processing and image recognition. The primary focus is technical, and students are expected to be able to program in Python or a similar language, and to be familiar with machine learning techniques such as classification and regression.
The course addresses several key aspects of the Nordic Nine
-- especially under Knowledge ("analytical with data and
curious about ambiguity" and "deep
Students are expected to work with large language models and other forms of generative AI in exercises, assignments, and exams. As with any other software, it should be clearly stated how the AI models are used in the performance of a given exercise, assignment, or exam.
|
||||||||||||||||||||||||||
Description of the teaching methods | ||||||||||||||||||||||||||
Lectures and weekly hands-on sessions with practical exercises. | ||||||||||||||||||||||||||
Feedback during the teaching period | ||||||||||||||||||||||||||
Students have hands-on exercises each week, where they receive in-person feedback from the teacher. They also receive feedback from regular online elements such as quizzes. Furthermore, they receive weekly written feedback on their work. Mid-way through the course, they create a plan for their project, and they receive feedback from the professor on their plan. There are also weekly office hours. | ||||||||||||||||||||||||||
Student workload | ||||||||||||||||||||||||||
|
||||||||||||||||||||||||||
Expected literature | ||||||||||||||||||||||||||
The literature can be changed before the semester starts. Students are advised to find the literature on Canvas before they buy the books.
Müller, A. C., & Guido, S. (2016). Introduction to machine learning with Python: a guide for data scientists. O'Reilly Media, Inc.
|