Download Selected Topics in Machine Learning and Reverse Engineering

Selected Topics in Machine Learning
and Reverse Engineering
Dozenten:
Prof. Dr. Fabian Theis
Raum: 02.08.039
Tel.: +49 89 289-17961,
Email: [email protected]
Prof. Dr. Oliver Junge
Raum: 02.08.040?
Tel.: +49 (89) 289 17987,
Betreuer:
Email: [email protected]
Daniel Schmidl
Raum: 02.08.039
Tel.: +49 89 289-17961,
Email: [email protected]
Dominik Wittmann
Raum: 02.08.039
Tel.: +49 89 289-17961,
Email: [email protected]
Termin für Vorbesprechung:
12.02.2010, 13:00 Uhr
Raum:
02.08.011
Verwendbarkeit:
Masterstudiengang Mathematik
Voraussetzungen:
Lineare Algebra I+II, Analysis I+II, Statistik 1, sowie Softwarekenntnisse in Matlab und R. Übung:
keine
Arbeitsaufwand:
ca. 90 h
Sprache:
Deutsch/Englisch
Im Zeitalter ständig wachsender Computerresourcen und Speichermöglichkeiten wird die Analyse riesiger
Datensätze immer wichtiger. Dies ist in den verschiedensten Bereichen, wie etwa auf dem Finanzmarkt, im
Marketing, in der Biologie oder auch der Medizin, von größtem Interesse. Der Kurs behandelt sowohl
überwachte-, wie auch unüberwachte Lernmethoden. Diese umfassen Principal Component und Independent
Component Analysen, k-means Algorithmen, Suport Vector Maschinen, Reinforcement Learning, aber auch
Verfahen zur Rekonstruktion von Systemen anhand von gemessener Daten über Zustände und Verhaltensweisen.
Der Fokus dieses Kurses liegt jedoch in der Einführung grundlegender Methoden zur Analyse großer
Datensätze.
Ziele und Ablauf: Die Teilnehmer sollen sich durch angeleitetes Selbststudium an eine wissenschaftliche
Arbeitsweise gewöhnen. Dazu bearbeitet jeder Student wöchentlich selbständig ein Thema. Dies wird durch das
Lösen von Übungsaufgaben zusätzlich vertieft. Jeweils ein Student fasst das Thema in einem ca. 60 minütigen
Vortrag zusammen, welcher unter anderem als Diskussionsgrundlage dienen soll. Neben der fachlichen
Thematik liegt das Ziel des Kurses vor allem in der Stärkung der Diskussionsfähigkeit.
Themen:
Machine Learning - Supervised Learning
Topic 0:
Overview of Supervised Learning - needs to be read by everyone before taking the course.
Literature:
C.M. Bishop; Pattern recognition and machine learning. Springer-Verlag, New York, 2006 –
chapter 1 & 2.
Topic 1:
Linear Models for Classification; discriminant functions, probabilistic generative models, and
Bayesian logistic regression.
Literature:
C.M. Bishop; Pattern recognition and machine learning. Springer-Verlag, New York, 2006 –
chapter 3.
Topic 2:
Kernel Methods; construction of kernels, radial basis functions, and Gaussian processes
Literature:
C.M. Bishop; Pattern recognition and machine learning. Springer-Verlag, New York, 2006 –
chapter 6.
Topic 3:
Sparse Kernel Machines; maximum margin classifier and relevance vector machines
Literature:
C.M. Bishop; Pattern recognition and machine learning. Springer-Verlag, New York, 2006 –
chapter 7.
Topic 4:
Graphical Models; Bayesian networks, Markov random fields, and inference in graphical
models
Literature:
C.M. Bishop; Pattern recognition and machine learning. Springer-Verlag, New York, 2006 –
chapter 8.
Topic 5:
Mixture Models and the Expectation Maximization (EM) Algorithm; K-means clustering,
mixture of Gaussians, and the EM algorithm
Literature:
C.M. Bishop; Pattern recognition and machine learning. Springer-Verlag, New York, 2006 –
chapter 9.
Topic 6:
Sequential Data; Markov models, hidden Markov models, and linear dynamical systems
Literature:
C.M. Bishop; Pattern recognition and machine learning. Springer-Verlag, New York, 2006 –
chapter 13.
Machine Learning – Unsupervised Learning
Topic 7:
Association Rules and Cluster Analysis; methods include basket analysis and data-clustering
by segmentation, hierarchical, and partitional clustering.
Literature:
T. Hastie, R. Tibshirani, and J. Friedman; The elements of statistical learning: data mining,
inference and prediction. Springer-Verlag, New York, 2001 – chapter 14.1 -14.3
Topic 8:
Reinforcement Learning; agent based action-reaction algorithms
Literature:
A. Barto and R. Sutton; Reinforcement Learning – An Introduction. MIT Press, Cambridge,
1998
D.P. Bertsekas and J. Tsitsiklis; Neuro-Dynamic Programming. Athena Scientific, Belmont,
1996.
Shalabh Bhatnagar, Richard S. Sutton, Mohammad Ghavamzadeh, and Mark Lee; Natural
actor–critic algorithms. Automatica, 2009.
H.R. Maei, C. Szepesvari, S. Bhatnagar, D. Silver, D. Precup, and R.S. Sutton; Convergent
Temporal-Difference Learning with Arbitrary Smooth Function Approximation. NIPS, 2009.
Topic 9:
Self Organizing Maps and Principal Component Analysis; dimension-reduction in highdimensional data-sets using eigenvalue decomposition or singular value decomposition.
Literature:
T. Hastie, R. Tibshirani, and J. Friedman; The elements of statistical learning: data mining,
inference and prediction. Springer-Verlag, New York, 2001 – chapter 14.4 and 14.5.
C.M. Bishop; Pattern recognition and machine learning. Springer-Verlag, New York, 2006 –
chapter 12.
Topic 10:
Independent Component Analysis; separating multivariate signals in additive subcomponents,
uniqueness of the factorization, analysis with additional noise in the data-set
Literature:
T. Hastie, R. Tibshirani, and J. Friedman; The elements of statistical learning: data mining,
inference and prediction. Springer-Verlag, New York, 2001 – chapter 14.6.
O. Hyvärinen; Independent component analysis. Neural Networks, 2000.
A. Hyvarinen J. Karhunen, and E. Oja; Independent Component Analysis. John Wiley &
Sons, 2001 - chapter 6.1, 6.3, 6.5 and 7.
Reverse Engineering
Topic 11:
Reconstruction of boolean models
Literature:
P. D’haeseleer, S. Liang, and R. Somogyi; Genetic network inference: from co-expression
clustering to reverse engineering. Bioinformatics, 2000.
S. Liang; Reveal, a general reverse engineering algorithm for interference of genetic network
architectures. Pacific Symposium on Biocomputing, 1998.
H. Lähdesmäki I. Shmulevich, and O. Yli-Harja; On Learning Gene Regulatory Networks
Under the Boolean Network Model. Machine Learning, 2003.
T. Akutsu, S. Miyano, and S. Kuhara; Inferring qualitative relations in genetic networks and
metabolic pathways. Bioinformatics, 2000.
Topic 12:
Reconstruction of polynomial models over finite fields
Literature:
Elena S. Dimitrova; Polynomial Models for Systems Biology: Data Discretization and Term
Order Effect on Dynamics. Dissertation, 2006 – chapter 1 – 4.
R. Lauenbacher and P. Mendes; A Discrete Approach to Top-Down Modeling of Biochemical
Networks. Computational Systems Biology, 2005.
Literature:
Books
A. Barto and R. Sutton; Reinforcement Learning – An Introduction. MIT Press, Cambridge, 1998
D.P. Bertsekas and J. Tsitsiklis; Neuro-Dynamic Programming. Athena Scientific, Belmont, 1996.
C.M. Bishop; Pattern recognition and machine learning. Springer-Verlag, New York, 2006.
T. Hastie, R. Tibshirani, and J. Friedman; The elements of statistical learning: data mining, inference
and prediction. Springer-Verlag, New York, 2001.
A. Hyvarinen J. Karhunen, and E. Oja; Independent Component Analysis. John Wiley & Sons, 2001.
Articles
O. Hyvärinen; Independent component analysis. Neural Networks, 2000.
Shalabh Bhatnagar, Richard S. Sutton, Mohammad Ghavamzadeh, and Mark Lee; Natural actor–critic
algorithms. Automatica, 2009.
H.R. Maei, C. Szepesvari, S. Bhatnagar, D. Silver, D. Precup, and R.S. Sutton; Convergent TemporalDifference Learning with Arbitrary Smooth Function Approximation. NIPS, 2009.
P. D’haeseleer, S. Liang, and R. Somogyi; Genetic network inference: from co-expression clustering to
reverse engineering. Bioinformatics, 2000.
S. Liang; Reveal, a general reverse engineering algorithm for interference of genetic network
architectures. Pacific Symposium on Biocomputing, 1998.
H. Lähdesmäki I. Shmulevich, and O. Yli-Harja; On Learning Gene Regulatory Networks Under the
Boolean Network Model. Machine Learning, 2003.
T. Akutsu, S. Miyano, and S. Kuhara; Inferring qualitative relations in genetic networks and metabolic
pathways. Bioinformatics, 2000.
Elena S. Dimitrova; Polynomial Models for Systems Biology: Data Discretization and Term Order
Effect on Dynamics. Dissertation, 2006 – chapter 1 – 4.
R. Lauenbacher and P. Mendes; A Discrete Approach to Top-Down Modeling of Biochemical Networks.
Computational Systems Biology, 2005.