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2017/2018  KAN-CEBUV2021U  Artificial Intelligence in the Marketplace

English Title
Artificial Intelligence in the Marketplace

Course information

Language English
Course ECTS 7.5 ECTS
Type Elective
Level Full Degree Master
Duration One Semester
Start time of the course Autumn
Timetable Course schedule will be posted at calendar.cbs.dk
Max. participants 70
Study board
Study Board for BSc/MSc in Business Administration and Information Systems, MSc
Course coordinator
  • Daniel Hardt - Department of Management, Society and Communication (MSC)
Administrative contact person is Jeanette Hansen, ITM (jha.itm@cbs.dk).
Changes in course schedule may occur
Tuesday 08.00-09.40, week 36-41, 43-51
Main academic disciplines
  • Information technology
  • Innovation
  • Language
Last updated on 13-02-2017

Relevant links

Learning objectives
To achieve the grade 12, students should meet the following learning objectives with no or only minor mistakes or errors:
  • Explain and analyze the key ideas and techniques underlying Artificial Intelligence technologies, including basic approaches to Machine Learning
  • Critically evaluate and compare the development and impact of Artificial Intelligence technologies in different business areas
  • Identify and analyze a specific problem which can be addressed by new Artificial Intelligence techniques
Artificial Intelligence in the Marketplace:
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 2-5
Size of written product Max. 15 pages
Assignment type Project
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 7-step scale
Examiner(s) Internal examiner and second internal examiner
Exam period Winter
Make-up exam/re-exam
Same examination form as the ordinary exam
Course content and structure

This course examines new Artificial Intelligence technologies that are rapidly transforming the digital marketplace. One highly publicized example is Watson, IBM’s intelligent question-answering system that won the game show Jeopardy! Another high-profile example is Google Translate, which now translates more text than all the world’s human translators. A few short years ago Artificial Intelligence technology was primarily relegated to the realm of fantasy and science fiction – now it is driving new businesses and technologies in a wide range of areas, such as e-discovery, sentiment analysis, topic tracking, and summarization. We will see that the sudden emergence of successful AI systems involves two key factors: the application of massive computing power, and leveraging the wisdom of crowds.
Facebook, Twitter and similar services are generating an enormous amount of data, covering every imaginable topic in thousands of different languages and styles. Artificial Intelligence is the key that can unlock the information in these massive unstructured collections of data. Students will get hands-on experience with newly developed tools for doing this. We will look at how AI technology is central to the strategies of IT Giants like Google, Microsoft, Facebook, Apple and IBM – and we will look at speculation about how these developments may well accelerate in the near future. A key point is that AI technologies are becoming widely available, with public descriptions and open-source implementations. This means that they are no longer the province of large powerful companies, and we will see how AI technologies are playing an increasingly important role now for many small companies. Students will have an opportunity with hands-on exercises to learn how to access and deploy many of the leading technologies.

Teaching methods
The class combines lectures, discussions and group work. Students will apply the course material in group presentations, and will get hands-on experience in the development, deployment and assessment of computational tools.
Feedback during the teaching period
Weekly feedback on hands-on exercises. Feedback on plans for final project. Weekly office hours.
Student workload
Lectures 24 hours
Workshops 12 hours
Class Preparation 110 hours
Exam and Preparation for Exam 60 hours
Total 206 hours
Expected literature

Asur and Huberman, 2010. Predicting the Future with Social Media. CoRR (10 pages)
Bollen et al. 2011. Twitter mood predicts the stock market, Journal of Computational Science, 2(1), March 2011, Pages 1-8
Grossman, L (2011)2045: The Year Man Becomes Immortal. Time Magazine. Feb. 10, 2011.
Halavy et al. (2009) The Unreasonable Effectiveness of Data. Intelligent Systems 24(2)
Michel et al. (2011) Quantitative Analysis of Culture Using Millions of Digitized Books. Science.
Joy, Bill (April 2000), Why the future doesn’t need us. Wired Magazine (Viking Adult) (8.04), ISBN 0670032492, retrieved 2007-08-07

Pang and Lee (2008) Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval 2.
Roush (2011) Inside Google’s Age of Augmented Humanity. Xconomy.com
Saunter.T. (2009) Assessing an Augmented Future.  Digital Cortex

Last updated on 13-02-2017