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## Machine Learning

In this course we will introduce the foundations of machine learning (ML). In particular, we will focus on understanding the theoretical aspects of ML that have made ML successful in a wide range of applications such as bioinformatics, computer vision, information retrieval, computer linguistics, robotics, etc.

The course gives a broad introduction into machine learning methods. After the lecture the students should be able to solve and analyze learning problems. The lecture is based on the previous machine learning courses offered by Matthias Hein and Peter Ochs.

The tentative list of** topics** cover:

- Probability theory
- Maximum Likelihood/Maximum A Posteriori Estimators
- Bayesian decision theory
- Linear classification and regression
- Model selection and evaluation
- Convex Optimization
- Kernel methods
- Societal Impact of Machine Learning
- Unsupervised learning (Clustering, Dimensionality Reduction)
- Introduction to Deep Learning

** Prerequisites: **The course is targeted to students in computer science, bioinformatics, math, and related areas with a mathematical background. Students should know linear algebra and have good basic knowledge of statistics, for example by having taken Mathematics for Computer Scientists I and II (for linear algebra) and Statistics Lab or Mathematics for Computer Scientists III (for statistics). In addition, prior attendance to machine learning related courses, e.g., Elements of Machine Learning, is considered as additional useful background.

__Organizational Information__

__Organizational Information__

**Lectures will start on April 13th!**

**Lectures*:** Mondays 14:15h at Building E2.5 -- Lecture hall I (0.01) & Thursdays at 12:15h at Building E2.5 -- Günter-Hotz Lecture hall I (0.01)

**Tutorials*:** To be decided.

__Bibliography__

__Bibliography__

[Bishop] Bishop, C. M. **Pattern recognition and machine learning**. Springer, 2006

[DSH] Duda, R. O., Hart, P. E., and Stork, D. G. **P attern classification (2nd edition).** Wiley-Interscience. 2000

[Boyd] Boyd, S., Boyd, S. P., and Vandenberghe, L. . **Convex optimization.** Cambridge university press, 2004

[eML] Hastie, T., Tibshirani, R., and Friedman, J. **The Elements of Statistical Learning**. Springer, 2009.

[Kernels] Smola, A. J., & Schölkopf, B. **Learning with kernels.** GMD-Forschungszentrum Informationstechnik, 1998.

[DL] Goodfellow, I., Courville, A., and Bengio, Y. **Deep learning**. MIT press, 2016.

[DL] Simon J.D. Prince **Understanding Deep Learning**. To be published by MIT press, 2023.