392232 Learning in Big Data Analytics (S) (WiSe 2021/2022)
Short comment
- Zoom Meeting
Contents, comment
-
The recent surge of machine learning (ML) has opened up various opportunities when analyzing big datasets. Beyond basic, non-ML supported techniques of big data analytics, such as identifying similar items in big datasets, or arranging how to distribute jobs on large compute clusters, for example, the ML supported techniques enable to extract knowledge from large datasets at utmost diversity and accuracy.
The seminar will start with a mini lecture. First, lectures will explain how to cluster datasets. Clustering is an 'unsupervised' machine learning technique by which to mine social network graphs, for example. Second, 'supervised' machine learning techniques (where 'deep learning' likely is the most prominent recent technique) and their use in analyzing big data will be discussed. The mini lecture will be followed by seminar presentations, to be presented in small groups of 4-5 students.
Teaching staff
Dates ( Calendar view )
| Frequency | Weekday | Time | Format / Place | Period |
|---|
Subject assignments
| Module | Course | Requirements | |
|---|---|---|---|
| 31-M-ASM2 Advanced Statistical Methods II | Veranstaltungen aus dem Bereich Statistik und/oder in (einem) methodisch verbundenen Gebiet(en) (I.) | Graded examination
|
Student information |
| 39-Inf-AB Algorithmen der Bioinformatik | Ausgewähltes Seminar zu Algorithmen der Bioinformatik | Study requirement
|
Student information |
| 39-Inf-SAB_a Spezielle Algorithmen der Bioinformatik | Ausgewähltes Seminar zu Spezielle Algorithmen der Bioinformatik | Study requirement
|
Student information |
The binding module descriptions contain further information, including specifications on the "types of assignments" students need to complete. In cases where a module description mentions more than one kind of assignment, the respective member of the teaching staff will decide which task(s) they assign the students.
| Degree programme/academic programme | Validity | Variant | Subdivision | Status | Semester | LP | |
|---|---|---|---|---|---|---|---|
| Studieren ab 50 |
E-Learning Space
A corresponding course offer for this course already exists in the e-learning system. Teaching staff can store materials relating to teaching courses there:
- Registered number: 15
- This is the number of students having stored the course in their timetable. In brackets, you see the number of users registered via guest accounts.
- Address:
- WS2021_392232@ekvv.uni-bielefeld.de
- This address can be used by teaching staff, their secretary's offices as well as the individuals in charge of course data maintenance to send emails to the course participants. IMPORTANT: All sent emails must be activated. Wait for the activation email and follow the instructions given there.
- If the reference number is used for several courses in the course of the semester, use the following alternative address to reach the participants of exactly this: VST_295002120@ekvv.uni-bielefeld.de
- Coverage:
- 14 Students to be reached directly via email
- Notes:
- Additional notes on the electronic mailing lists
- Email archive
- Number of entries 0
- Open email archive
- Last update basic details/teaching staff:
- Saturday, October 9, 2021
- Last update times:
- Monday, June 21, 2021
- Last update rooms:
- Monday, June 21, 2021
- Type(s) / SWS (hours per week per semester)
- S / 2
- Language
- This lecture is taught in english
- Department
- Faculty of Technology
- Questions or corrections?
- Questions or correction requests for this course?
- Planning support
- Clashing dates for this course
- Links to this course
- If you want to set links to this course page, please use one of the following links. Do not use the link shown in your browser!
- The following link includes the course ID and is always unique:
- https://ekvv.uni-bielefeld.de/kvv_publ/publ/vd?id=295002120
- Send page to mobile
-
Click to open QR code
Scan QR code:
- ID
- 295002120