|
|
| 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
|
| 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
|
| 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 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 |
| 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 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
|
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