2024/2025 KAN-CDSCO2004U Machine Learning and Deep Learning
English Title | |
Machine Learning and Deep Learning |
Course information |
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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 |
Master of Science (MSc) in Business Administration and Data
Science
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Course coordinator | |
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Main academic disciplines | |
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Teaching methods | |
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Last updated on 12-09-2024 |
Relevant links |
Learning objectives | ||||||||||||||||||||||||||||
Students should meet the following learning
objectives:
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Course prerequisites | ||||||||||||||||||||||||||||
This course requires a fundamental understanding
of:
1. Probability 2. Statistics 3. Python programming. |
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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 assignment is max. 5 pages + appendix, written in a group of 2-4 students. The students have to get 2 out of 3 assignments approved to go to the exam. Process: 1. Exam papers of compulsory assignments will be uploaded into Canvas. 2. Students will upload their reports. 3. Finally, the feedback will ONLY be visible via Canvas. |
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Examination | ||||||||||||||||||||||||||||
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Course content, structure and pedagogical approach | ||||||||||||||||||||||||||||
The course provides knowledge of various concepts, techniques and methods related to machine learning and deep learning. More specifically, it contains
Furthermore, the course provides the students with practical hands-on experience in machine learning using open-source libraries such as scikit-learn. After completing the course, the students will be able to apply and use various machine-learning techniques on real datasets. |
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Description of the teaching methods | ||||||||||||||||||||||||||||
The course consists of lectures, exercises, and
mandatory assignments. The lectures will ONLY be delivered offline
and the hands-on exercise sessions will be conducted on campus.
There will be teaching assistant(s) providing technical support for
the hands-on exercise sessions.
The presented theories, concepts and methods should be applied in practice in the exercise sessions. The students will work on the mandatory assignments to consolidate their understanding of the concepts and the application of the concepts using the practical skills obtained from the hands-on exercises. |
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Feedback during the teaching period | ||||||||||||||||||||||||||||
During the hands-on exercises following each lecture, the students will receive help and feedback in solving the practical hands-on exercises from the teacher and the teaching assistant(s). | ||||||||||||||||||||||||||||
Student workload | ||||||||||||||||||||||||||||
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Expected literature | ||||||||||||||||||||||||||||
Research articles, and other study materials will be uploaded (to the Canvas) during the course.
Primary Text Book: Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. "O'Reilly Media, Inc.". ISBN: 9781492032649. |
Last updated on
12-09-2024