Graduate (CUGS, CIS, ...) students interested in the application of machine learning techniques to advanced system performance optimization, as in compiler construction, library generation, runtime systems, parallel computing, software engineering, system simulation and optimization.
Interested undergraduate students are also welcome, as long as there are free seats left.
Lectures (ca. 15h), optional theoretical exercises for self-assessment, student projects and/or presentations. Written exam.
The course was last given
This is a new course.
The course introduces fundamental techniques of machine learning and considers case studies for its application in automated system performance tuning, such as auto-tuning library generators, compilers, and runtime systems.
Linear algebra. Discrete mathematics. Data structures and algorithms. Some basic knowledge of computer architecture is assumed. For the case study presentations, some background in at least one application area, such as compiler construction, library generation, signal processing software, runtime systems, or software composition, is required.
"[Machine] learning is the process of [automatically] constructing, from training data,
a fast and/or compact surrogate function that heuristically solves
a decision, prediction or classification problem for
which only expensive or no algorithmic solutions are known.
It automatically abstracts from sample data to a total decision function."
- [Danylenko et al., Comparing Machine Learning Approaches..., SC'2011, LNCS 6708]
Lecture block (2.5 days, room Donald Knuth), presentation session (0.5 days, room Donald Knuth) and exam (0.5 days, room von Neumann) in Linköping.
Organization and overview|
Decision Tree Learning
|C. Kessler||Neural Networks:
Biological Neural Networks; McCulloch-Pitts units; Perceptron; Competitive Learning; Feed-Forward Networks; Backpropagation Algorithm
Application Area: Automated Performance Tuning
Adaptive sampling and decision tree learning for optimized composition
Demo: C4.5 (Lu Li)
Selection of papers or projects for student presentations
|W. Löwe||Decision diagrams
Demo rapid miner
If you prefer working with a textbook, you may e.g. use the ones mentioned below as additional reading. Note that most books cover much more than what we can go through in our introductory course.
As a good introductory textbook we recommend
Other useful books: (e.g. for further reading)
Further literature references will be added later.
TEN1: Written exam (Welf, Christoph) 1.5p.
3p if both examination moments are fulfilled. Admission to the exam requires attendance in 50% of the lectures and lessons.
New course 2012.
Machine Learning course at Control Engineering, ISY, Linköping University, given every other year. More mathematically than algorithmically oriented, and more details compared to our more light-weight course.
... list to be continued ...