|Teaching Assistants:||Simone Parisi, Filipe Veiga|
|Lectures:||Tuesdays, 13:30-15:10 in Room S202/C120|
|Wednesdays, 15:20-16:05 in Room S202/C110|
|Office Hours:||Simone Parisi, Wednesday, 11:00 - 12:00|
|Filipe Veiga, TBA|
|TU-CAN:||20-00-0358-iv Machine Learning: Statistical Approaches|
|Exam:||July, 18, 12:30 - Room S101/A1|
As the World Wide Web keeps growing, computer science keeps evolving from is traditional form, slowly slowly becoming the art to create intelligent software and hardware systems that draw relevant information from the enormous amount of available data.
Why? Let's look at the facts: billions of web pages are at our disposal, videos with an accumulated time of 20 hours are uploaded every minute on Youtube and the supermarket chain Walmart alone performed more than one million transactions per hour, creating a database of more than 2.5 petabytes of information. John Naisbitt has stated the problem very clearly:
"We are drowning in information and starving for knowledge."
In the future of computer science, machine learning will therefore be an important core technology. Not only that, machine learning already is the technology which promises the best computer science jobs. Hal Varian, the Chief Engineer of Google in 2009 depicted it like this:
"I keep saying the sexy job in the next ten years will be statisticians and machine learners. People think I am joking, but who would have guessed that computer engineers would have been the sexy job of the 1990s? The ability to take data, to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it, that is going to be a hugely important skill in the next decades. "
Accordingly, this lecture serves as an introduction to machine learning. Special emphasis is placed on a clear presentation of the lectures contents supplemented by small sample problems regarding each of the topics. The teacher pays particular attention to his interactions with the participants of the lecture, asking multiple question and appreciating enthusiastic students.
The course gives a introduction to statistical machine learning methods. The following following topics are expected to be covered throughout the semester.
Math classes from the bachelor's degree, basic programming abilities, introductionary classes to computer science.
The most important books for this class are:
Additionally, the following books might be useful for specific topics:
Jan Peters heads the Intelligent Autonomous Systems Lab at the Department of Computer Science at the TU Darmstadt. Jan has studied computer science, electrical, control, mechanical and aerospace engineering. You can find Jan Peters in the Robert-Piloty building in S2 | 02 find in room E314. You can also contact him through
Filipe Veiga earned his master's degree in electrical engineering and information technology from IST Lisbon, Portugal. He is a PhD student in IAS since September 2013 and works on robot grasping and manipulation using tactile sensing. You can find Filipe in the Robert-Piloty building S2 | room E325. You can also contact him through
For further inquiries do not hesitate to contact us immediately!