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Course Syllabus |
Course policies and information. |
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This course teaches the practical application of various tools and techniques for data analysis and machine learning. The focus here is on the application on various, real world datasets.
We focus on three aspects during this course:
- Introduction to Python including extensive programming exercises
- Data Visualization and Analysis
- Hands-On Machine Learning
This course has mandatory attendance in ALL classes. You may miss only 4 classes.
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Attendance checks will be performed every week!
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If you are facing a situation with a prospective absence, that exceeds the stated limits, speak to the course instructor without any further delay.
All defined and communicated dates (submissions, presentation, etc.) are strict and no extensions are granted.
In case extensions are granted due to unforeseen situations these will be valid for all and not for individual students only. Such a case will be communicated via e-mail.
The final grade is composed by four individual components:
| Format | Exam modality | Weighting (%) |
|---|---|---|
| Checkups | Regular Checkups during class | 25 |
| Lab Assignments | Tick-List and submission | 10 |
| Participation | Oral Questions to Reading+Wrap Up and Presentation of the lab submission | 25 |
| Project | Presentation and Submission | 40 |
Checkups: short closed-book checkups. May be between 3--5.
Lab Assignments: at least 50% of the lab assignments have to be completed in order to get a positive grade.
Participation: Questions to each reading and wrap-up exercises or to lab assignments. This is in an interview/presentation style. Not being able to answer the questions leads to a deduction of the points. In case of the lab assignment presentation, also the points for the ticks will not be awarded.
Project: Introduction to the project given at a later point in time. See class schedule
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| Upper Bound (incl.) | Lower Bound ( excl.) | Grade |
|---|---|---|
| 100 % | 87.5 % | 1 |
| 87.5 % | 75 % | 2 |
| 75 % | 62.5 % | 3 |
| 62.5 % | 50 % | 4 |
| 50 % | 5 |
The content of the lecture matierial is based on the following ressources:
- W3Schools
- Grokking Machine Learning (Luis G. Serrano)
- Hands-On Machine Learning with Scikit-Learn and TensorFlow (Aurelien Geron)
- http://neuralnetworksanddeeplearning.com/
I can only be contacted on my SUAS e-mail address, as I do not check my PLUS address on regular basis.
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I only answer e-mails from PLUS adresses. E-Mails from private adresses may be dropped by the incoming mail server, overseen or mistakingly considered as spam.
To book a time-slot with me, please use this link.