Learning objectives 
 Access the fundamental challenges of machine learning such as
model selection, model complexity, etc.
 Understand the underlying mathematical relationships within and
across machine learning algorithms
 Characterize the strengths and weaknesses of various machine
learning approaches and algorithms
 Design, implement, analyse and apply different data mining,
machine learning techniques and deep learning techniques for
big/business datasets in organizational contexts and for realworld
applications
 Summarize the application areas, trends, and challenges in data
mining and machine learning
 Critically assess the ethical and legal issues in applying
machine learning algorithms
 Exhibit deeper knowledge and understanding of the topics as
part of the project and the report should reflect on critical
awareness of the methodological choices with written skills to
accepted academic standards.

Course prerequisites 
This course requires a fundamental understanding
of programming in Python language as achieved in, or comparable to,
Foundations of Business Data Analytics: Architectures, Statistics
and Programming course from 1st semester of Cand.Merc.IT (Data
Science). 
Prerequisites for registering for the exam
(activities during the teaching period) 
Number of compulsory
activities which must be approved: 2
Compulsory home
assignments
Each assignment is 13 pages in group of 14 students.
The students have to get 2 out of 4 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 
Data Mining,
Machine Learning and Deep 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. 
Individual or group exam 
Individual oral exam based on written group
product 
Number of people in the group 
24 
Size of written product 
Max. 15 pages 
Assignment type 
Project 
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 
7point grading scale 
Examiner(s) 
Internal examiner and second internal
examiner 
Exam period 
Summer 
Makeup exam/reexam 
Same examination form as the ordinary exam
Students can submit the same project
or they can choose to submit a revised
project.


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
 Basics of Data mining and machine learning
 Strengths and weaknesses of Dimensionality Reduction
Algorithms: variance thresholds,Correlation Thresholds, Principal
Component Analysis (PCA), Linear Discriminant Analysis (LDA)
 Linear models for regression such as maximum likelihood,
sequential learning, regularized least squares
 Linear models for classification such as linear classification,
logistic regression, support vector machines
 Classification models such as probabilistic generative models,
probabilistic discriminative models
 Unsupervised learning: clustering, probabilistic clustering,
ExpectationMaximization Algorithm.
 Neural Networks: feedforward neural networks, network
training, backpropagation, convolutional neural networks
 Deep Learning: deep feedforward networks, regularization for
deep learning, optimization for training deep models, application
of deep learning
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 
Feedback on mandatory assignments will provided
in general 
Student workload 
Lectures 
24 hours 
Exercises 
24 hours 
Prepare to class 
48 hours 
Project work & report 
100 hours 
Exam and prepare 
10 hours 
Total 
206 hours 

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:

Authors(s)

Title

Publisher/ ISBN/ DOI

[AIAMA]

Russell, Stuart J., and Peter Norvig.

Artificial intelligence: a modern approach.

Malaysia; Pearson Education Limited, 2016.

[CIMI]

Kruse, Rudolf, Christian Borgelt, Christian Braune, Sanaz
Mostaghim, and Matthias Steinbrecher.

Computational intelligence: a methodological
introduction.

Springer, 2016.

[CML]

Hal Daumé III

A Course in Machine Learning



[DMCT]

Jiawei Han, Micheline Kamber, Jian Pei

Data Mining: Concepts and Techniques

Morgan Kaufmann; 3 edition (July 6, 2011)
ISBN13: 9789380931913

[ESL]

Friedman, Jerome, Trevor Hastie, and Robert Tibshirani.

The Elements of Statistical Learning: Data Mining, Inference,
and Prediction

Second Edition, Springer; 2nd edition (2016). ISBN13:
9780387848570

[HML]

Aurélien Géron

HandsOn Machine Learning with ScikitLearn and TensorFlow:
Concepts, Tools, and Techniques to Build Intelligent
Systems


[IDM]

PangNing Tan, Michael Steinbach, Vipin Kumar

Introduction to Data Mining

Pearson; 1 edition (May 12, 2005), ISBN13:
9780321321367

[ISL]

Gareth James, Daniela Witten, Trevor Hastie, Robert
Tibshirani

An Introduction to Statistical Learning

Springer
ISBN 9781461471370

[MLAPP]

Kevin P. Murphy

Machine Learning: A Probabilistic Perspective

The MIT Press

[MMD]

Leskovec, Jure, Anand Rajaraman, and Jeffrey David
Ullman.

Mining of massive datasets.

Cambridge university press, 2014.

[PDSH]

Jake VanderPlas

Python Data Science Handbook: Essential Tools for Working with
Data

Oreilly, ISBN13: 9781491912058
Online Book Link:
https://jakevdp.github.io/PythonDataScienceHandbook/

Notes, articles, chapters and webpages will be handed out/made
available during the
course
