Lecturer: | Prof. Dr. Benno Stein |
Advisor: | Michael Völske, Janek Bevendorff |
Workload: | 6 ECTS |
Open to: | M.Sc. (CS4DM, CSM, MI, HCI, DE) |
Venue: | SR3.09, S143 |
Kick-off meeting: | Monday, April 4th, 2022. |
Regular sessions: | Monday 13:30 |
Moodle: | moodle.uni-weimar.de/course/view.php |
Information on the web is growing at an exponential pace, courtesy of social media platforms, blogs, and news. Such large scale data sources call for high-end, scalable, distributed architectures for cognitive analysis, which shape the business decisions of many industries. In addition, deep learning has been propelled into mainstream and is now accessible to researchers and companies alike, thanks to tools such as TensorFlow, PyTorch. The Webis research group operates large-scale high-performance compute infrastructure (totaling more than 3000 CPU cores, 10+ Petabytes of storage, and 24 high-end GPUs), which will be put to use in the course of this seminar. Students will receive application-oriented training in Big data and deep learning frameworks, language technologies, and explore interesting research questions. This seminar requires good skills in both programming (Python) and algorithms.
Please note that this course requires prior Python programming experience. In addition, some familiarity with Linux environments, and knowledge of machine learning basics, is highly recommended. To help you gauge your prior subject knowledge, we've provided a set of self-assessment questions below. Read through the self-assessment questions, and take note of how many you can answer in the affirmative, and how many answers you know without having to look them up.
This questionnaire is not perfectly suitable for studying in order to catch up; however, the questions should cover a broad range of topics around our course's scope and highlight potential weak points.
In order to successfully complete this course, you will have to
Date | Title | Description | Materials | Deliverables | Stream |
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04.04.2022 | Deep Learning in Python (Session 1) |
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11.04.2022 | Deep Learning in Python (Session 2) |
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18.04.2022 | No Session (Easter Monday) | ||||
25.04.2022 | Deep Learning in Python (Session 3) |
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[lab] | |
02.05.2022 | Deep Learning on SLURM (Session 1) |
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09.05.2022 | Deep Learning on SLURM (Session 2) |
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Set up Cluster Access | ||
16.05.2022 | Project Fair |
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23.05.2022 | Prompt Engineering (Session 1) |
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30.05.2022 | Prompt Engineering (Session 2) |
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06.06.2022 | No Session (Whit Monday) | ||||
13.06.2022 | Prompt Engineering (Session 3) |
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Prompt Engineering Presentations | |
20.06.2022 | Q&A Session |
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Project Exposé | ||
27.06.2022 | Group Meetings |
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04.07.2022 | Mid-Term Presentations |
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Project Presentation | ||
11.07.2022 | Q&A Session |
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29.08.2022 | Project Deadline | Hand in your report in PDF format by eMail. Cutoff is 22:00 CEST | Project Report |
William E. Shotts. The Linux Command Line: A Complete Introduction. 2nd ed. No Starch Press, Incorporated, 2019. http://linuxcommand.org/tlcl.php.
Matotek, Turnbull, Lieverdink. Pro Linux System Administration. Apress, 2017.
Leskovec, Rajaraman, Ullman. Mining of Massive Datasets. Cambridge University Press, 2014. http://infolab.stanford.edu/~ullman/mmds/book.pdf
Tom White. Hadoop: The Definitive Guide, 4th ed. O'Reilly Media, 2015. ISBN: 9781491901687.
Manning, Raghavan, Schütze. Introduction to Information Retrieval. Cambridge University Press, 2008. http://nlp.stanford.edu/IR-book/
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