Teaching
No current teaching.
Guest Lecturing
Florian Verhein has given the following guest lectures (coursework):
University of Sydney, Australia
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COMP5318 Knowledge Discovery and Data Mining: Clustering (Week 11, Semester 2, 2006)
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COMP5338 Advanced Data Models: Association Rule Mining (Week 6, Semester 2, 2006).
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COMP5318 Knowledge Discovery and Data Mining: Association Rule Mining (Week 8, Semester 1, 2007)
Additionally, the tutoring Florian did for COMP3310/COMP3610 (Theory of Computation, see below) was in a lecture format.
University of Stuttgart, Germany
Tutoring
Florian has tutored the following courses:
University of Sydney, Australia
Course Information
Please see official websites for up to date information. This is a summary and is accurate at the time these were tutored.
COMP5318: Knowledge Discovery and Data Mining
Current website.
This course gives a comprehensive coverage of well known techniques in Data Mining and Machine Learning. If focuses on the following three techniques:
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Clustering
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K-Means
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Bisecting K-Means
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Hierarchical
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DBSCAN (Density Based Spatial Clustering of Applications with Noise)
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Fuzzy c-means
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Expectation Maximisation (EM) Algorithm
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Self Organising Networks (SOM)
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Classification
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Rule based
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Nearest neighbour
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Naive Bayes
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Baysian Networks
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Decision Trees
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Neural Networks
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Support Vector Machines (SVM)
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Association Rule Mining and Sequential Pattern Mining
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Apriori
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FP-Growth
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GSP (Generalised Sequential Pattern)
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Prefix-Span
COMP3310/3610: Theory of Computation
Current website.
This course has 3 parts:
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Computability
- Models of computation, from automata to Turing machines,
- The halting problem and other not-computable problems,
- The Church-Turing thesis,
- Connections to logical theories
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Complexity
- Complexity theory: what can we compute?
- Easy problems and hard problems,
- Computational hardness,
- Reductions
- Complexity classes and hierarchies,
- The P versus NP problem
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Algorithms
- Dealing with hard problems,
- Approximation algorithms,
- Randomized algorithms
