Inhalt des Dokuments
This practice-oriented course is ideal for beginners who are looking to be quickly introduced to data science. At its end, you will be equipped with the toolset needed to analyze, understand and gain new insights from data. You will learn how to use Python coupled with Jupyter to manipulate and visualize data. You will peek into the theoretical foundation of machine learning and understand its main algorithms. Furthermore, you will apply your knowledge on real world problems by using machine learning to classify and predict data.
Learning Goals and Syllabus
At the end of this course you will be able to:
- Code in Python
- Manipulate and visualize data using numpy, pandas, matplotlib and sci-kit learn
- Run exploratory analysis on data and gain new insights
- Understand the theoretical foundation of machine learning
- Apply machine learning to predict and classify data
- Understand and apply Linear regression, K-Means Clustering, PCA, Decision Trees and Neural Networks
Please click here to see the syllabus.
The course will include the following topics:
- Python basics
- Data cleaning and manipulation
- Introduction to machine learning
- Unsupervised methods
- Introduction to state of the art methods
The general prerequisites of the TU Berlin Summer University are the following: at least one year of university experience + English level B2 or equivalent.
Basic programming knowledge is also required for this course. Students should be able to write and run small programs in the language of their choice. Students should also have basic knowledge in linear algebra and statistics/probability theory and know what loops, conditionals, methods/functions, libraries, vectors, matrices, gradient and probability distributions are.
Additionally students are requested to bring their own laptop.
Dennis Grinwald specializes in Machine Learning theory and application. He currently works as a Research Assistant in the Machine Learning Group at Fraunhofer HHI, Berlin, where he is focussed on tackling the problem of finding new algorithms that increase the efficiency of Distributed Deep Neural Networks. Dennis has significant experience in both research and teaching and in addition to his role at Fraunhofer HHI, he is currently a graduate (final year) student in Computer Science at TU Berlin.
Please direct questions about the course to the TU Berlin Summer University Team at: firstname.lastname@example.org . We will answer your questions and direct specific queries regarding course content to the course lecturers where necessary.