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Instructors: Dr. Timo Baumann
Event type:
Lecture
Displayed in timetable as:
ML-VL
Hours per week:
4
Credits:
6,0
Language of instruction:
English
Min. | Max. participants:
- | 60
Comments/contents:
In many applications and domains, massive amounts of data are
collected and processed every day. To be able to make efficient use of
such data, there is an urgent need for tools to extract important
pieces of information from the flood of unimportant details.
Machine learning is a relatively young discipline that tries to deal
with this problem, by designing algorithms to analyze large amounts of
complex data in a principled way. Machine learning is the core
technique in many applications such as spam filtering, object
recognition, analyzing user preferences, recommender systems, and so
on. Scientific disciplines such as biology,
neuroscience, physics, or medicine discover the potential of machine
learning methods for analyzing their empirical data. And, last but not
least, many large companies like google, Amazon, facebook heavily rely
on machine learning techniques.
The field of machine learning combines ingredients from several
fields: most importantly, we need to design efficient algorithms to
process the amount of data, and we need to ensure
that predictions made by machine learning algorithms are statistically
sound.
The focus of the lecture is on algorithmic aspects of machine
learning. We will cover many of the standard algorithms and learn
about the general principles for building good machine
learning algorithms.
<ul>
<li>Supervised learning problems: Linear methods; regularization;
non-linear kernel methods </li>
<li> Unsupervised learning problems: Dimension reduction
((kernel) PCA, multi-dimensional scaling, manifold methods) </li>
<li> How to model machine learning problems:
Bayesian decision theory, loss functions, feature selection,
evaluation and comparison of algorithms. Common pitfalls. </li>
<li> Reinforcement learning: </li>
<li> The following topics are NOT going to be covered: decision trees,
graphical models, Bayesian approaches to machine
learning. You can learn about some of
them in other courses in the department (e.g. Prof. Menzel,
Prof. Wermter). </li>
</ul>
More information can be found on the course webpage, you will find a link at
https://tams.informatik.uni-hamburg.de/lectures/2018ss/ML
Learning objectives:
- Students get to know the most important classes of modern machine
learning algorithms.
- They are going to develop an understanding why certain algorithms
work well and others don't.
- They learn how to evaluate and compare the results of different
learning algorithms.
- They learn how to model machine learning problems and what are
common pitfalls
Didactic concept:
- The course is going to be taught by Prof. Ulrike von Luxburg (first 9 weeks) and Prof. Jianwei Zhang (last 3 weeks).
- Course language is english.
- This course is offered both for computer science students (full semester, 9 CP) and for maths students (2/3 of the semester, 6 CP). Please come to the first lecture to understand the implications.
- The course consists of the lectures (4 SWS) and the Tutorials (Übungen, 2 SWS). The assignments involve theoretical assignments and implementation assignments (programming language: MATLAB/Octave, will be introduced at the beginning).
- Prerequisites: this course involves a certain amount of mathematical modeling, in particular linear algebra and probability theory, as taught in the "maths for computer science students" classes in the first semesters.
Literature:
See the course webpage for detailed links to literature.
Additional examination information:
Exam will be written, details to be announced.
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