Editor, Bureaucrats, Administrators
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{{Announcement|Note: The room for this course has changed to the bigger room 0.101!)}} | |||
{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}} | |||
== Details == | == Details == | ||
{{CourseDetails | {{CourseDetails | ||
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|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke | |module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke | ||
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/david_koll Dr. David Koll] | |lecturer=[http://www.net.informatik.uni-goettingen.de/people/david_koll Dr. David Koll] | ||
|ta= | |ta= | ||
|time= | |time=Thursday, 14-16 | ||
|place= | |place=Ifi 0.101 | ||
|univz= | |univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=203182&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung] | ||
}} | }} | ||
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* Exploratory data analysis | * Exploratory data analysis | ||
* The Python Data Science stack | * The Python Data Science stack | ||
* How to deal with unbalanced data | |||
* Advanced algorithms for Data Science (an overview of competition winning algorithms) | * Advanced algorithms for Data Science (an overview of competition winning algorithms) | ||
* Parameter tuning for predictive models | * Parameter tuning for predictive models | ||
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==Prerequisites== | ==Prerequisites== | ||
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Coursera Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.) and a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.). | *You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Coursera Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.) and a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.). | ||
*Knowledge of Python | *Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries | ||
== | ==Schedule== | ||
{| {{Prettytable|width=}} | {| {{Prettytable|width=}} | ||
|- | |- | ||
|{{Hl2}} |'''When?''' | |{{Hl2}} |'''When?''' | ||
|{{Hl2}} |'''What?''' | |{{Hl2}} |'''What?''' | ||
|- | |- | ||
| align="right" | 19.10.2017 | | align="right" | 19.10.2017 | ||
| Lecture 1: Introduction & The Data Science Pipeline | | Lecture 1: Introduction & The Data Science Pipeline - Task 1: Release | ||
|- | |- | ||
| align="right" | 26.10.2017 | | align="right" | 26.10.2017 | ||
| Lecture 2: The Python Data Science Stack | | Lecture 2: The Python Data Science Stack | ||
|- | |- | ||
| align="right" | 02.11.2017 | | align="right" | 02.11.2017 | ||
| Task 1: Intermediate meeting | | Task 1: Intermediate meeting | ||
|- | |- | ||
| align="right" | 09.11.2017 | | align="right" | 09.11.2017 | ||
| No lecture | | No lecture | ||
|- | |- | ||
| align="right" | 16.11.2017 | | align="right" | 16.11.2017 | ||
| | | Task 1: Presentation of Exemplary Solution // Task 2: Release | ||
|- | |- | ||
| align="right" | 23.11.2017 | | align="right" | 23.11.2017 | ||
| | | Lecture 3: Advanced Algorithms for Data Science | ||
|- | |- | ||
| align="right" | 30.11.2017 | | align="right" | 30.11.2017 | ||
| Lecture | | Lecture 4: Evaluation and Tuning of Models | ||
|- | |- | ||
| align="right" | 07.12.2017 | | align="right" | 07.12.2017 | ||
| Task 2: Intermediate meeting | | Task 2: Intermediate meeting | ||
|- | |- | ||
| align="right" | 14.12.2017 | | align="right" | 14.12.2017 | ||
| | | No lecture | ||
|- | |- | ||
| align="right" | 21.12.2017 | | align="right" | 21.12.2017 | ||
| Task 2: Presentations // Task 3: Release | | Task 2: Presentations // Task 3: Release | ||
|- | |- | ||
| align="right" | 04.01.2018 | | align="right" | 04.01.2018 | ||
| No lecture | | No lecture | ||
|- | |- | ||
| align="right" | 11.01.2018 | | align="right" | 11.01.2018 | ||
| Task 3: Intermediate meeting I | | Task 3: Intermediate meeting I | ||
|- | |- | ||
| align="right" | 18.01.2018 | | align="right" | 18.01.2018 | ||
| No lecture | | No lecture | ||
|- | |- | ||
| align="right" | 25.01.2018 | | align="right" | 25.01.2018 | ||
| Task 3: Intermediate meeting II | | Task 3: Intermediate meeting II | ||
|- | |- | ||
| align="right" | 01.02.2018 | | align="right" | 01.02.2018 | ||
| No lecture | | No lecture | ||
|- | |- | ||
| align="right" | 08.02.2018-22.02.2018 | | align="right" | 08.02.2018-22.02.2018 | ||
| Task 3: Presentations | | Task 3: Presentations | ||
|- | |- | ||
| align="right" | 31.03.2018 | | align="right" | 31.03.2018 | ||
| Final Report deadline | | Final Report deadline | ||
|- | |- | ||
|} | |} |