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Maties Machine Learning https://mml-stellenbosch.github.io/ Meet the MML Research Groups Arina Britz Bruce Watson Corn van Daalen Hugo Touchette Sugnet Lubbe Trienko Grobler cair CENTRE FOR ARTIFICIAL INTELLIGENCE

  1. Maties Machine Learning https://mml-stellenbosch.github.io/

  2. Meet the MML Research Groups ● Arina Britz ● Bruce Watson ● Corné van Daalen ● Hugo Touchette ● Sugnet Lubbe ● Trienko Grobler

  3. cair CENTRE FOR ARTIFICIAL INTELLIGENCE RESEARCH Knowledge Abstraction, Representation and Reasoning in Artificial Intelligence Arina Britz Centre for AI Research, Dept of Information Science, Stellenbosch Univ, South Africa abritz@sun.ac.za 2019

  4. Cognitive Computational Logics Logics for Cognition and AI: ◮ description logics; ◮ modal logics Knowledge Abstraction and Representation: ◮ language design; ◮ expressivity; ◮ applications Reasoning: ◮ entailment; explanation; learning; debugging; interoperability; ... cair CENTRE FOR ARTIFICIAL INTELLIGENCE RESEARCH

  5. Future Perspectives Reasoning support: ◮ extending tools with non-classical reasoning capabilities; ◮ knowledge abstraction from data Methodological support: ◮ ontology debugging, revision, repair; ◮ domain visualisation and exploration Integration of KR with other formalisms: ◮ Integration with formal concept lattices; ◮ Integration with machine learning; ◮ Explainable AI cair CENTRE FOR ARTIFICIAL INTELLIGENCE RESEARCH

  6. cair CENTRE FOR ARTIFICIAL INTELLIGENCE RESEARCH Applied Algorithmics Bruce W. Watson Information Science Chairman, Centre for AI Research Centre for Knowledge-Dynamics & Decision-Making bwwatson@sun.ac.za 2019

  7. Algorithmics Correctness-by-construction (CbC) ◮ Calculus for algorithms and programs, correctness proof ◮ Used in conjunction with verification and testing ◮ Extend into X-by-C and parallelism Inventive algorithmics. . . come up with entirely new ones ◮ Use CbC to invent entirely new algorithms ◮ Stringology ◮ Finite automata ◮ Glass box knowledge representation (lattices) Domain-specific implementation techniques ◮ Finite state techniques ◮ IoT, FPGA, GPU cair CENTRE FOR ARTIFICIAL INTELLIGENCE RESEARCH

  8. AI & Algorithmics for Cybersecurity Pattern correlation ◮ Network traffic learning and scanning ◮ Distributed pattern correlation ◮ Complex Event Processing Generation of cyberweaponry ◮ Machine learning for hybridizing Decision- and supply-chains ◮ Group decision support & vulnerabilities ◮ Signing of the silicon IP supply chain Cryptography ◮ CbC for post-quantum cryptography cair CENTRE FOR ARTIFICIAL INTELLIGENCE RESEARCH

  9. People with the department A mix of internal/extern, all with Ph.D’s and mostly professors Loek Cleophas (Eindhoven) — computing scientist, engineer Fritz Solms (S-plane) — physicist, software engineer Tinus Strauss (Pretoria) — computer scientist, gentleman scientist Derrick Kourie (Pretoria) — computer scientist, OR/stats Norbert Gronau (Potsdam) — computer engineer, i4.0, head of institute Martin Berglund (Munich) — computer scientist, mathematician Jackie Daykin (KCL, Wales) — mathematician, met Erdos Bruce W. Watson — computing scientist/engineer, discrete mathematician, chip nerd cair CENTRE FOR ARTIFICIAL INTELLIGENCE RESEARCH

  10. Probabilistic Reasoning and Planning for Mobile Robots Probabilistic Reasoning and Planning for Mobile Robots Corn´ e van Daalen Electronic Systems Laboratory (ESL) Department of Electrical and Electronic Engineering http://staff.ee.sun.ac.za/cvdaalen/ cvdaalen@sun.ac.za 1 March 2019

  11. Applications Probabilistic Reasoning and Planning for Mobile Robots

  12. Overview and Approach Probabilistic Reasoning and Planning for Mobile Robots Sensors: stereo cameras, lidar, radar Problem: autonomous navigation mapping, localisation, planning large, online, uncertain Approach: probabilistic modelling inference and planning under uncertainty probabilistic graphical models (PGMs)

  13. Projects Probabilistic Reasoning and Planning for Mapping: Mobile Robots 3D mapping with uncertain robot pose Detection of moving objects using stereo vision Multi-object tracking with radar sensors Modelling of and inference in a semantic map Localisation: SLAM with limited resources Semantic SLAM for drone localisation

  14. Projects Probabilistic Reasoning and Planning for Mobile Robots Planning: Robot planning under uncertainty Probabilistic collision prediction Probabilistic reasoning (general): Robust inference

  15. Research on stochastic processes Hugo Touchette Applied Mathematics Stellenbosch University A T P H A T = a L x H t L x ( t ) t t a • Noisy systems (Markov process) • Rare events (fluctuations) • Rare transitions (jumps, crashes, etc.) • Simulations and sampling • Prediction, estimation Hugo Touchette (Stellenbosch) Stochastic processes 1 / 2

  16. Current projects P H A T = a L x H t L P ( A T = a ) ≈ e − TI ( a ) t a Large deviations • Students: • Compute I ( a ) for specific models • Johan Du Buisson MSc Phy • Markov processes, spectral methods • Stuart Reid MSc AM • Wessel Blomerus MSc AM • Faith Msibi Hons AM Rare event simulations • Efficient sampling of P ( A T = a ) • Post-doc: • Daniel Nickelsen • Monte Carlo, importance sampling SU Phy • Collaborators: Change point detection • Raphael Chetrite U Nice • Find locations where statistics change • Arnaud Guyader U Paris • Grant Rotskoff NYU • ML prediction, sequence modelling Hugo Touchette (Stellenbosch) Stochastic processes 2 / 2

  17. Multivariate visualisation put to practice

  18.  Visualisation of multivariate data based on some dissimilarity  Biplots Multi-  Subset of MDS dimensional  Simultaneous display of rows and columns of a data matrix  Simplest example: PCA biplot scaling  Challenges: big data MDS  Long data – represent cloud of points with 𝛽 -bags  Wide data - ???

  19.  Niël le Roux & Sugnet Lubbe  Book on Canonical Analysis with John Gower (UK)  Visualization of class separation in the two-group case  Computing and validating neural reliability measures derived from EEG recordings with Pieter Schoonees (Erasmus University) Projects  High throughput sequencing data  Compositional data  Clustering / Classification  Functional data analysis

  20.  Johané Nienkemper-Swanepoel (PhD)  Visualization for categorical data with missing values  Adriaan Rowen (Masters)  Unravelling black box machine learning methods using biplots  Raeesa Ganey (PhD) Projects  Principal surface biplots  Replace linear PCA with nonparametric manifold “following” the data  Sasol Technologies R&D  Ruan Rousseuw (PhD)  Visualisation for online process monitoring  André Mostert (PhD)  Machine learning in multivariate process control

  21. Applying machine learning to solve problems in remote sesnsing, trajectory mining and interferometry T.L. Grobler tlgrobler@sun.ac.za Autor: 27.02.19

  22. Settlement detection Remote Sensing The art of converting data about the Earth‘s surfice recorded with distant platforms into usable inforation. Autor: 27.02.19

  23. Trajectory mining Submitted conference paper The automatic identification system (AIS) is an automatic tracking system that uses Transponders on ships and is used by vessel traffic services (VTS). Problems: 1. Trajectory mining. 2. Data representation. 3. Ship tracking. 4. Anonmoly detection. 5. Route Prediction. Autor: 27.02.19

  24. Interferometry Connecting antennas together to form a single telescope whose purpose is to observe celestial radio emission. Autor: 27.02.19

  25. Radio Frequency Interference Typical Problems Atenna failure analysis Lydia de Lange (18350070@sun.ac.za) Radio Galaxy Classification Danie Ludick (dludick@sun.ac.za) Burger Becker (17522021@sun.ac.za) Autor: 27.02.19

  26. Autor: 27.02.19

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