|
|
| 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 IT Mangement
(ITM)
|
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
15-04-2015
|
| Learning objectives |
To achieve the grade 12, students
should meet the following learning objectives with no or only minor
mistakes or errors:
- Independently identify, select and combine key theoretical
concepts taught in the course, and apply them on real business
cases
- Explain in depth, assess and analyze the key ideas and
techniques underlying Artificial Intelligence technologies
- Critically evaluate and compare the development and impact of
Artificial Intelligence technologies in different business
areas
- Identify and analyze the strategies and plans of leading
businesses with respect to Artificial Intelligence technology
- Identify and analyze a specific problem which can be addressed
by new Artificial Intelligence techniques
|
| Examination |
|
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 |
| 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 |
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 recently 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. |
| Student workload |
| Lectures |
24 hours |
| Class Preparation |
123 hours |
| Exam and Preparation for Exam |
60 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
|
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