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Written on 06.11.25 by Frances Yung There were a few cases of slightly late submission of Assignment 1, which we accepted exceptionally since you were still getting used to the CMS. There were a few cases of slightly late submission of Assignment 1, which we accepted exceptionally since you were still getting used to the CMS. |
Talk today, Oct 31, by Doug Leasure on "Agile population nowcasting for Gaza in a rapidly changing "Written on 31.10.25 by Ingmar Weber As mentioned in the guest lecture yesterday: Douglas Leasure (https://www.demography.ox.ac.uk/people/douglas-leasure) from the University of Oxford will give a talk today, Friday, October 31 on his work on population mapping in Gaza. The lecture will start at 1pm (sharp) in building E1.7, 3rd… Read more As mentioned in the guest lecture yesterday: Douglas Leasure (https://www.demography.ox.ac.uk/people/douglas-leasure) from the University of Oxford will give a talk today, Friday, October 31 on his work on population mapping in Gaza. The lecture will start at 1pm (sharp) in building E1.7, 3rd floor, room 3.23. Feel free to join if you'd like to learn more about Doug's work in humanitarian contexts. |
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Written on 29.10.25 by Frances Yung There has been slight revision on Assignment 1 yesterday. Please refer to the Forum post for details. |
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Written on 24.10.25 by Frances Yung Hi all, |
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Written on 23.10.25 by Frances Yung Hi all, Hi all, |
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Written on 17.10.25 by Frances Yung Hi All, |
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Written on 10.10.25 (last change on 10.10.25) by Frances Yung The first lecture starts on 16.Oct Thursday 12:00pm at E1.3 HS002. |
Elements of Data Science and Artificial Intelligence
Artificial intelligence is a long-standing branch of computer science concerned with the design of algorithms and systems exhibiting intelligent behavior. Data science is a comparatively young area concerned with the extraction of knowledge and insights from structured and unstructured data. Increasingly, the real power of computer science applications lies in combining the two, exploiting insights from data to take intelligent decisions.
Both artificial intelligence (AI) and data science (DS) are complex multi-disciplinary scientific fields. This course provides an overview of central concepts and ideas, structured and motivated by prominent applications requiring elements from both DS and AI. As a headstarter, we provide a crash course on Python in the first three lectures. Then, we will introduce how data science and AI can be used to research questions at the intersection of society and technology, encompassing both (i) computing of society, i.e., using computational methods to understand societal phenomena, and (ii) computing for society, i.e., using digitally assisted interventions to improve society. This will be followed by a brief introduction to machine learning (ML), which lies at the heart of the intersection between DS and AI. We will cover autonomous driving as a prominent application of sensing, system design, control, and learning, as well as neural language models and the associated learning and reasoning techniques for natural language processing. We finally cover the computer-accelerated drug design (CADD) and ML techniques for prioritizing molecules.
The aim is for students to understand the scope of DSAI and to obtain intuitions about its central algorithmic elements. Detailed technical expositions and analyses of these elements are not covered; these are the subject of later more specialized courses.
The course is accompanied by exercises, covering technical concepts through examples, as well as posing simple programming exercises (suitable for first-term students) in the Python language.