Vorlesung Advanced Machine Learning
|Dozenten:||Dr. Abdolreza Nazemi;|
|Termin:||Mi 14:00 - 15:30|
|Ort:||20.21 RZ Raum 217|
Important Information:In accordance with the announcement of Prof. Alexander Wanner, KIT Vice President for Higher Education and Academic Affairs, Academic education operations will start punctually on April 20, 2020 with the planned beginning of the lecturing period of the summer semester.
However, digital courses exclusively will be offered until further notice. Dear students, please do not come to the campus for courses or for learning! The KIT departments, central facilities, and business units, and not least the lecturers are doing their best at the moment to let as many courses as possible take place online in parts or completely this summer semester.
This course will be a combination of live tutorial sessions via Zoom and recorded videos for the lecture.
Time: Every week on Wed., 02:00 PM to 03:30 PM
First Session: 22.04.2020
Join Zoom Meeting: https://kit-lecture.zoom.us/j/94876294900
Meeting ID: 948 7629 4900
Please download and import the following iCalendar (.ics) files to your calendar system. Weekly:
Zoom Information for Tutorial:
Time: Bi-weekly on Wed., 03:45 PM to 05:15 PM
First Session: 06.05.2020
Join Zoom Meeting: https://kit-lecture.zoom.us/j/97824065640
Meeting ID: 978 2406 5640
Course Description:In recent years, the volume, variety, velocity, veracity, and variability of available data have increased due to improvements in computational and storage power. The rise of the Internet has made available large sets of data that allow us to use and merge them for different purposes. Data science helps us to extract knowledge from the continually-increasing large datasets. This course will introduce students to a wide range of machine learning and statistical techniques such as deep learning, LASSO, and support vector machines. You will get familiar with text mining, and the tools you need to analyze the various facets of data sets in practice. Students will learn theory and concepts with real data sets from different disciplines such as finance, marketing, and business.
- Alpaydin, E. (2014). Introduction to Machine Learning. Third Edition, MIT Press.
- De Prado, M. L. (2018). Advances in Financial Machine Learning. John Wiley & Sons.
- Goodfellow, I., Bengio, Y., and A. Courville (2017). Deep Learning. MIT Press.
- Hastie, T., Tibshirani, R., and J. Friedman (2009). Elements of Statistical Learning. Second Edition. Springer.
- James, G., Witten, D., Hastie, T., and R. Tibshirani (2013). An Introduction to Statistical Learning: with Applications in R. Springer.
- HWitten, I. H., Eibe, F., Hall, M. A., Pal, C. J. (2016). Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann.
|22.04.2020, 14:00 - 15:30||Lecture1 Introduction|
|29.04.2020, 14:00 - 15:30|
|06.05.2020, 14:00 - 15:30|
|13.05.2020, 14:00 - 15:30|
|20.05.2020, 14:00 - 15:30|
|27.05.2020, 14:00 - 15:30|
|03.06.2020, 14:00 - 15:30|
|10.06.2020, 14:00 - 15:30|
|17.06.2020, 14:00 - 15:30|
|24.06.2020, 14:00 - 15:30|
|01.07.2020, 14:00 - 15:30|
|08.07.2020, 14:00 - 15:30|
|15.07.2020, 14:00 - 15:30|
|22.07.2020, 14:00 - 15:30|
|Lecture1 Introduction||Nazemi, Abdolreza|
|Lecture 6||Nazemi, Abdolreza|
|Lecture 5||Nazemi, Abdolreza|
|Lecture 4||Nazemi, Abdolreza|
|Lecture 3||Nazemi, Abdolreza|
|Lecture 2||Nazemi, Abdolreza|
|Lecture 7||Nazemi, Abdolreza|