This course gives you context and first-hand experience with the two major catalyzers of the computational science revolution: big data and artificial intelligence. With more than 99% of all mediated information in digital format and with 98% of the world population using digital technology, humanity produces an impressive digital footprint. In theory, this provides unprecedented opportunities to understand and shape society. In practice, the only way this information deluge can be processed is through using the same digital technologies that produced it. Data is the fuel, but machine learning it the motor to extract remarkable new knowledge from vasts amounts of data. Since an important part of this data is about ourselves, using algorithms in order to learn more about ourselves naturally leads to ethical questions. Therefore, we cannot finish this course without also talking about research ethics and about some of the old and new lines computational social scientists have to keep in mind. As hands-on labs, you will use IBM Watson’s artificial intelligence to extract the personality of people from their digital text traces, and you will experience the power and limitations of machine learning by teaching two teachable machines from Google yourself.
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UC Davis, one of the nation’s top-ranked research universities, is a global leader in agriculture, veterinary medicine, sustainability, environmental and biological sciences, and technology. With four colleges and six professional schools, UC Davis and its students and alumni are known for their academic excellence, meaningful public service and profound international impact.
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- 3 stars4.50%
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BIG DATA, ARTIFICIAL INTELLIGENCE, AND ETHICS의 최상위 리뷰
This course is really very interesting.I have learned a lot about big data,artificial intelligence and ethics.
Great course with awesome insights into modern day AI use, with interesting practical application also.
i m very grateful to learn big things in simple way, with experienced prof from various streams. impact was mind storming by peer reviews and solving assignment.
I really enjoyed Martin's presentation style and his clear enthusiasm and knowledge about the topic, thank you.
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For more information please view the Computational Social Science Trailer
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