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Honours/Summerscholars Projects
Generic Projects
The Statistical Machine Learning program of National ICT Australia has a number of researchers willing to supervise honours and summer-scholarship projects. Check out some of the examples below, or feel free to suggest projects in the areas of:
- Transforming non-linear problems to easier problems in higher dimensions (Kernel methods)
- Statistical analysis on the building blocks of life (Bio-informatics)
- Analysing text documents to answer authorship and other questions
- Real life applications of advanced statistical methods
- Estimating probability distributions
- Optimising a function by gradient methods
- Training computers with rewards and punishments (Reinforcement Learning)
Or any application of these fields. You can either contact a researcher directly, or email doug dot aberdeen at anu.edu.au.
Supervisor: Nic Schraudolph
SML_01: Fish and Chips: Driving Nemo: Teach a robotic fish on wheels some new moves
SML_02: Machine Learning Go: Learn from millions of recorded games how to play the game of Go (several projects possible)
Supervisor: Doug Aberdeen
SML_08: Probabilistic temporal planning: Help us build a better Microsoft Project
SML_10: Ro-Sham-Bo: Build an AI rock-paper-scissors player.
SML_13: Practical Traffic Light Signalling Scheme optimisation: Making Sydney's traffic flow smoothly!
Supervisor: Olivier Buffet
AI_P01: Factored Planning: Developping planning algorithms for networked systems.
SML_12: Reinforcement Learning for Robot Control: Get a robot to plan its actions in an uncertain environment.
Supervisor: Alex Smola and S V N Vishwanathan
SML_14: Fast SSE3 Integer Kernel: ''Chik (SRS)'' A high performance integer multiplication kernel for the X86 architecture.
SML_15: Python Package Manager: An open source package manager for installing and distributing Python packages.
SML_16: High Performance Spam Filter: Develop a high speed and high accuracy spam filter for mail servers.
SML_17: Nearest Neighbors: Implement an efficient nearest neighbor algorithm for the world's best machine learning library in Python.
SML_18: Decision Trees and Forests: Generate decision trees and forests in Python and C for CREST, our machine learning library.
Supervisor: Adam Kowalczyk
SML_19: Fighting Cancer: ''Ho (SRS)'' Build software which can detect cancer.
Supervisor: Li Cheng
SML_21: Motion Detection: Detect moving objects using a moving camera in real time
SML_22: Action recognition: Teach a computer to tell if a person is walking, running, or dancing
SML_23: Novelty detection from video data: Identify novel situations from video data