29 January 2021
Machine Learning using Python: Theory and Applicationonline course
This course will focus on the theory and application of the most common machine learning models. The students will be using the Python programming language in order to implement the taught models and apply them to solve real world problems. Through practical assignments, and a project on real world data the students will learn to independently recognize problems that can be solved via machine learning, choose the right machine learning algorithm to use, and evaluate their results correctly. The machine learning models that will be covered throughout the course include: Linear Regression, Logistic Regression, Support Vector Machines, Principal Component Analysis, Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, and more.
Reading week: January 4th - January 8th, 2021. Flexible, 5- 10 hours preparatory work to be done on-demand.
Online course: January 11th - January 29th, 2021. Estimated meeting times: Mondays through Fridays. Exact session times will be confirmed once registrations have closed (sessions will be scheduled according to the timezones of the registered course participants). Should you have any questions regarding the course timetable, please contact us at email@example.com
Please note this is a full-time, intensive course. Weeks 1-3 will involve approximately 30 hours of workload.
Dennis Grinwald is a Research Assistant in the Machine Learning group at Fraunhofer HHI and a final year graduate student in Computer Science, with specialization in machine learning and artificial intelligence.
This course is designed for current university students, working professionals and any individuals with an interest in furthering their knowledge and skills in programming with Python for Data Science and Machine Learning.
Participants from all fields and disciplines are welcome.
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.
- Theory and tricks of the trade for training the most common machine learning models
- Implement machine learning algorithms using Sklearn, Scikit-learn, PyTorch
- Handle and preprocess data using famous Python data science packages NumPy and Pandas
- Learn to visualize and interpret the results of the trained machine learning models
EUR 920: student price
EUR 1320: Working professional/Non-student