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Advanced Machine Learning

Advanced Machine Learning
type: Vorlesung (V) links:
semester: SS 2020
time: 2020-04-22
14:00 - 15:30 wöchentlich
20.40 Architektur, Hörsaal Nr. 9 (HS9)
20.40 Architekturgebäude


2020-04-29
14:00 - 15:30 wöchentlich
20.40 Architektur, Hörsaal Nr. 9 (HS9)
20.40 Architekturgebäude

2020-05-06
14:00 - 15:30 wöchentlich
20.40 Architektur, Hörsaal Nr. 9 (HS9)
20.40 Architekturgebäude

2020-05-13
14:00 - 15:30 wöchentlich
20.40 Architektur, Hörsaal Nr. 9 (HS9)
20.40 Architekturgebäude

2020-05-20
14:00 - 15:30 wöchentlich
20.40 Architektur, Hörsaal Nr. 9 (HS9)
20.40 Architekturgebäude

2020-05-27
14:00 - 15:30 wöchentlich
20.40 Architektur, Hörsaal Nr. 9 (HS9)
20.40 Architekturgebäude

2020-06-03
14:00 - 15:30 wöchentlich
20.40 Architektur, Hörsaal Nr. 9 (HS9)
20.40 Architekturgebäude

2020-06-10
14:00 - 15:30 wöchentlich
20.40 Architektur, Hörsaal Nr. 9 (HS9)
20.40 Architekturgebäude

2020-06-17
14:00 - 15:30 wöchentlich
20.40 Architektur, Hörsaal Nr. 9 (HS9)
20.40 Architekturgebäude

2020-06-24
14:00 - 15:30 wöchentlich
20.40 Architektur, Hörsaal Nr. 9 (HS9)
20.40 Architekturgebäude

2020-07-01
14:00 - 15:30 wöchentlich
20.40 Architektur, Hörsaal Nr. 9 (HS9)
20.40 Architekturgebäude

2020-07-08
14:00 - 15:30 wöchentlich
20.40 Architektur, Hörsaal Nr. 9 (HS9)
20.40 Architekturgebäude

2020-07-15
14:00 - 15:30 wöchentlich
20.40 Architektur, Hörsaal Nr. 9 (HS9)
20.40 Architekturgebäude

2020-07-22
14:00 - 15:30 wöchentlich
20.40 Architektur, Hörsaal Nr. 9 (HS9)
20.40 Architekturgebäude


lecturer: Dr. Abdolreza Nazemi
sws: 2
lv-no.: <a target="lvn" href="https://campus.studium.kit.edu/events/7zwdCP4fQxWU8lREwg8DRA">2540535</a>
Notes

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 machine. 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 marketing, finance, and business.

Tentative Course Outline:

  • Introduction
  • Statistical Inference
  • Shrinkage Methods
  • Model Assessment and Selection
  • Tree-based Machine Learning Algorithms
  • Dimensionality Reduction
  • Neural Networks and Deep Learning
  • Natural Language Processing with Deep Learning
  • Support Vector Machine

Time of attendance

  • Attending the lecture: 13 x 90min = 19h 30m
  • Attending the exercise classes: 7 x 90min = 10h 30m

The student will learn

  • A wide range of machine learning algorithms and their weaknesses.
  • The fundamental issues and challenges: data, high-dimension, train, model selection, etc.
  • How to imply machine learning algorithms for real-world applications.
  • The fundamentals of deep learning, main research activities, and on-going research in this field.
Bibliography
  • 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. (online available)
  • Hastie, T., Tibshirani, R., and J. Friedman (2009). Elements of Statistical Learning. Second Edition. Springer. (online available)
  • Leskovec, J., Rajaraman, A., Ullman, J. D., (2014). Mining of Massive Datasets. Cambridge University Press. (online available)
  • Witten, I. H., Eibe, F., Hall, M. A., Pal, C. J. (2016). Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann.