64-360 Lecture Machine Learning

Course offering details

Instructors: Prof. Dr. Victor Emanuel de Atocha Uc Cetina

Event type: Lecture

Displayed in timetable as: ML-VL

Hours per week: 4

Credits: 6,0

Language of instruction: English

Min. | Max. participants: - | 80

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.

Appointments
Date From To Room Instructors
1 Tue, 15. Oct. 2019 08:15 09:45 B-201 Prof. Dr. Victor Emanuel de Atocha Uc Cetina
2 Th, 17. Oct. 2019 08:15 09:45 D-125/129 Prof. Dr. Victor Emanuel de Atocha Uc Cetina
3 Tue, 22. Oct. 2019 08:15 09:45 D-125/129 Prof. Dr. Victor Emanuel de Atocha Uc Cetina
4 Th, 24. Oct. 2019 08:15 09:45 D-125/129 Prof. Dr. Victor Emanuel de Atocha Uc Cetina
5 Tue, 29. Oct. 2019 08:15 09:45 D-125/129 Prof. Dr. Victor Emanuel de Atocha Uc Cetina
6 Tue, 5. Nov. 2019 08:15 09:45 D-125/129 Prof. Dr. Victor Emanuel de Atocha Uc Cetina
7 Th, 7. Nov. 2019 08:15 09:45 D-125/129 Prof. Dr. Victor Emanuel de Atocha Uc Cetina
8 Tue, 12. Nov. 2019 08:15 09:45 D-125/129 Prof. Dr. Victor Emanuel de Atocha Uc Cetina
9 Th, 14. Nov. 2019 08:15 09:45 D-125/129 Prof. Dr. Victor Emanuel de Atocha Uc Cetina
10 Tue, 19. Nov. 2019 08:15 09:45 D-125/129 Prof. Dr. Victor Emanuel de Atocha Uc Cetina
11 Th, 21. Nov. 2019 08:15 09:45 D-125/129 Prof. Dr. Victor Emanuel de Atocha Uc Cetina
12 Tue, 26. Nov. 2019 08:15 09:45 D-125/129 Prof. Dr. Victor Emanuel de Atocha Uc Cetina
13 Th, 28. Nov. 2019 08:15 09:45 D-125/129 Prof. Dr. Victor Emanuel de Atocha Uc Cetina
14 Tue, 3. Dec. 2019 08:15 09:45 D-125/129 Prof. Dr. Victor Emanuel de Atocha Uc Cetina
15 Th, 5. Dec. 2019 08:15 09:45 D-125/129 Prof. Dr. Victor Emanuel de Atocha Uc Cetina
16 Tue, 10. Dec. 2019 08:15 09:45 D-125/129 Prof. Dr. Victor Emanuel de Atocha Uc Cetina
17 Th, 12. Dec. 2019 08:15 09:45 D-125/129 Prof. Dr. Victor Emanuel de Atocha Uc Cetina
18 Tue, 17. Dec. 2019 08:15 09:45 D-125/129 Prof. Dr. Victor Emanuel de Atocha Uc Cetina
19 Th, 19. Dec. 2019 08:15 09:45 D-125/129 Prof. Dr. Victor Emanuel de Atocha Uc Cetina
20 Tue, 7. Jan. 2020 08:15 09:45 D-125/129 Prof. Dr. Victor Emanuel de Atocha Uc Cetina
21 Th, 9. Jan. 2020 08:15 09:45 D-125/129 Prof. Dr. Victor Emanuel de Atocha Uc Cetina
22 Tue, 14. Jan. 2020 08:15 09:45 D-125/129 Prof. Dr. Victor Emanuel de Atocha Uc Cetina
23 Th, 16. Jan. 2020 08:15 09:45 D-125/129 Prof. Dr. Victor Emanuel de Atocha Uc Cetina
24 Tue, 21. Jan. 2020 08:15 09:45 D-125/129 Prof. Dr. Victor Emanuel de Atocha Uc Cetina
25 Th, 23. Jan. 2020 08:15 09:45 D-125/129 Prof. Dr. Victor Emanuel de Atocha Uc Cetina
26 Tue, 28. Jan. 2020 08:15 09:45 D-125/129 Prof. Dr. Victor Emanuel de Atocha Uc Cetina
27 Th, 30. Jan. 2020 08:15 09:45 D-125/129 Prof. Dr. Victor Emanuel de Atocha Uc Cetina
Exams in context of modules
Module (start semester)/ Course Exam Date Instructors Compulsory pass
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Instructors
Prof. Dr. Victor Emanuel de Atocha Uc Cetina