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Lehrveranstaltung

Vorlesung Advanced Machine Learning

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Semester:Sommersemester 2019
Dozenten:Dr. Abdolreza Nazemi;
Termin:Mittwoch 15:45 - 17:15
Ort:Geb. 30.34, Lichttechnik-Hörsaal (LTI)
SWS:2

Teilnehmen

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Inhalt

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 finance, marketing, and business.



Literature:
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.
Witten, I. H., Eibe, F., Hall, M. A., Pal, C. J. (2016). Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann.

Veranstaltungstermine

Time Inhalt
24.04.2019, 14:00 - 15:30 Lecture1 Introduction
08.05.2019, 15:45 - 17:15
15.05.2019, 14:00 - 15:30
22.05.2019, 15:45 - 17:15
29.05.2019, 15:45 - 17:15
05.06.2019, 15:45 - 17:15
12.06.2019, 15:45 - 17:15
19.06.2019, 15:45 - 17:15
26.06.2019, 15:45 - 17:15
03.07.2019, 15:45 - 17:15
10.07.2019, 15:45 - 17:15
17.07.2019, 15:45 - 17:15
24.07.2019, 15:45 - 17:15


Vorlesungsmaterialien

Inhalt Autor
Lecture12 NLP and LSTM 2

Lecture11 NLP and LSTM

Lab Exercise 2

NN and DNN

Lecture9 SVM

Unsupervised Learning

Lab Exercise 1

Lecture7 Tree-Based Methods Nazemi, Abdolreza

Lecture6 Regularization Nazemi, Abdolreza

Lecture5 Resampling

Lecture4 Classification

Lecture3 Multiple Linear Regression

Basic Definitions and MLR

Lecture1 Introduction