2021/2022 KANCDSCO1004U Data Mining, Machine Learning, and Deep Learning
English Title  
Data Mining, Machine Learning, and Deep 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 
Master of Science (MSc) in Business Administration and Data
Science

Course coordinator  


Main academic disciplines  


Teaching methods  


Last updated on 24062021 
Relevant links 
Learning objectives  


Course prerequisites  
This course requires a fundamental understanding of programming in Python language as achieved in, or comparable to "Foundations of Data Science: Programming and Linear Algebra" at 1st semester CM (data science).  
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 13 pages in group of 14 students. The students have to get 2 out of 3 assignments approved in order to go to the exam. There will not be any extra attempts provided to the students before the ordinary exam. If a student cannot hand in due to documented illness, or if a student does not get the activity approved in spite of making a real attempt, then the student will be given one extra attempt before the reexam. Before the reexam, there will be one home assignment (max. 10 pages) which will cover 2 mandatory assignments. 

Examination  


Course content, structure and pedagogical approach  
The course provides knowledge of various concepts, techniques and methods related to data mining, machine learning and deep learning approaches. Furthermore, it introduces
Furthermore, the course provides the students with practical handson experience on data mining and machine learning using open source machine learning libraries such as scikitlearn in Python programming language. After completing the course, the students will be able to apply and use various data mining and machinelearning techniques on realword big/business datasets. 

Description of the teaching methods  
The course consists of lectures, exercises, and
assignments. Each lecture is followed by an exercise session, and
there will be a teaching assistant providing technical support for
assignments and course projects.
The presented theories, concepts and methods should be applied in practice and exercise sessions. The students work in the entire semester on a mini project displaying the understanding of the concepts presented in the lectures and exercises. CBS Learn is used for sharing documents, slides, exercises etc. as well as for interactive lessons if applicable. 

Feedback during the teaching period  
In this course, feedback to the students will be
provided in the following ways.
1) During the handson exercises following each lecture, the students will receive help and feedback in solving the practical handson exercises from the teacher and the instructors. 2) At the end of each exercise session, we will go through the solutions to the exercises and discuss various techniques and alternative methods to solve the exercises and also clarify any questions from the students. 3) Feedback on the mandatory assignments will be provided to students as part of the grading for the mandatory assignments. Since the mandatory assignments are at the group level, the students will receive collective feedback on their group submission. 

Student workload  


Expected literature  
The literature can be changed before the semester starts. Students are advised to find the final literature on Canvas before they buy the books.
Text Books:
Notes, articles, chapters and webpages will be handed out/made available during the course 