DS817: Algorithms we live by

Study Board of Science

Teaching language: English
EKA: N340068102
Assessment: Second examiner: Internal
Grading: 7-point grading scale
Offered in: Odense
Offered in: Spring
Level: Master

STADS ID (UVA): N340068101
ECTS value: 5

Date of Approval: 06-11-2019

Duration: 1 semester

Version: Archive

Entry requirements

The course cannot be taken by students enrolled in the master programme in Computer Science.

Academic preconditions


Course introduction

In this course we will examine human and artificial intelligence comparatively. We will look at state-of-the-art cognitive models that describe how humans cope with a wide range of demanding tasks. Then we will use that knowledge as a lever to understand machine learning models for dealing with the same problems (or vice versa). The topics discussed will include, among others, categorization and forecasting, recommendation, ranking and search, optimization and planning, creativity, as well as different flavors of multi-armed bandits and reinforcement learning. We will examine scenarios where humans are assisted by artificial intelligence, and problems where humans compete against algorithms. Further, we will discuss whether human cognitive processes can be recovered from the behavioral traces that people leave behind on the Internet; Finally, we will look at problems where human intelligence can provide insights and inspiration for the development of new methods in the quest for better AI agents.

In relation to the competence profile of the degree it is the explicit focus of the course to: 

The students will become familiar with seminal algorithms that are deployed in online interfaces and govern our every-day life and will learn how people adapt their decision-making processes to these algorithms. We will discuss problems commonly encountered by start-up companies (e.g. in the e-commerce or media domains) but also by individual decision makers and organizations more broadly. The acquired competencies will be useful for people planning to work as product managers, data analysts or data scientists in the industry. 

Expected learning outcome

The learning objective of the course is that the student demonstrates the ability to:

By the end of the course students are expected to develop a better understanding machine learning algorithms and human cognition.They should be able to have a good grasp of cognitive models that describe human behavior and machine learning models for solving practical problems encountered by individuals and organizations such as categorization, estimation, ranking, clustering etc. 


The following main topics are contained in the course:

The exploration-exploitation trade-off: when should people or algorithms try new things and when should they settle for tested courses of action?
Supervised learning and the bias/variance dilemma: what are the main approaches in estimation and categorization in machine learning? What does it mean for a model to overfit the data? What are good ways to get around this problem? 
Unsupervised learning and natural language processing: Can AI learn without a teacher telling it what is right or wrong? How can this be achieved? 
Humans collaborating and competing with AI: What techniques where used in Deep Blue when it won Kasparov? What techniques did IBM use to win in Jeopardy? Similarly, what type of AI algorithms were used by Deepmind to win human champions in the game of Go? How can humans and machines effectively combine their distinct intelligences?  
Human behavior and big data: What can big data teach us about human behavior? How are our data used by companies to personalize their products to us? 


See itslearning for syllabus lists and additional literature references.

Examination regulations

Exam element a)




Written exam




Second examiner: Internal


7-point grading scale


Student Identification Card


Normally, the same as teaching language


2 hours

Examination aids

Not allowed. A closer description of the exam rules will be posted in itslearning.

ECTS value


Additional information

The examination form for re-examination may be different from the exam form at the regular exam.

Indicative number of lessons

140 hours per semester

Teaching Method

The teaching method is based on three phase model.
  • Intro and Skills training phase: 102 lectures (including optional exercises.)
Activities during the study phase:
  • 38 hours preparation for the exams

Teacher responsible

Name E-mail Department
Pantelis Pipergias Analytis pantelis@sam.sdu.dk Institut for Marketing & Management


Administrative Unit

Institut for Matematik og Datalogi (datalogi)

Team at Educational Law & Registration


Offered in


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