This page contains possible data sets, ideas for questions and code that you can use for your project.  Since this is a research oriented class, it is highly encouraged to pick a project related to your own research, which is not limited by this page.   

Ideally, we prefer group sizes of 2-3 people.  Exceptions possible with instructor's permission.  Please feel free to contact either Andreas, Daniel or Deb about project ideas.   

Data sets

NLP & Text data

Image & Video data

Neuroscience & Physiology data

Collaborative prediction data

Sensor network data

Network data

Other sources of data

Project ideas

Online learning, bandit optimization, dimension reduction

  • Compare online classification / regression with offline algorithms on data sets
  • Experiment with parallel online learning:
    • Martin Zinkevich, Alexander Smola, John Langford. Slow learners are fast. NIPS 2009
  • Online multitask learning, e.g., for link prediction / collaborative filtering (e.g., on Netflix data)
    • Weinberger, K., Dasgupta, A., Attenberg, J., Langford, J., and Smola, A., Feature Hashing for Large Scale Multitask Learning, International Conference on Machine Learning, 2009
  • Implement and evaluate some nonstandard bandit algorithm (X-armed bandit [A.2], contextual bandits [A.4], [A.1] ...)
    • S. Bubeck, R. Munos, G. Stoltz, C. Szepesvari. "Online Optimization in X-Armed Bandits" NIPS 2008.
    • J. Langford and T. Zhang. "The Epoch-Greedy Algorithm for Contextual Multi-armed Bandits" NIPS 2007.
    • S. Pandey, D. Chakrabarti, D. Agrawal. "Multi-armed Bandit Problems with Dependent Arms" ICML 2006
  • Compare different bandit algorithms (e.g., low regret algorithms like UCB with optimal solution using MDPs / Gittins indices)
    • J. C. Gittins, D. M. Jones, A Dynamic Allocation Index for the Discounted Multiarmed Bandit Problem, Biometrika, Vol 66, No. 3. (1979), pp. 561-565.
  • Experiment with Gaussian process optimization, compare different selection heuristics
    • D. Lizotte, T. Wang, M. Bowling, D. Schuurmans. "Automatic Gait Optimization with Gaussian Process Regression" IJCAI 2007
    • Niranjan Srinivas, Andreas Krause, Sham M. Kakade, Matthias Seeger. "Gaussian Process Bandits without Regret: An Experimental Design Approach" arXiv [pdf]
  • Random features -- an "explicit" kernel trick.  Compare against standard, kernelized SVMs in runtime / accuracy
    • Random Features for Large-Scale Kernel Machines, Ali Rahimi, Ben Recht,
      in Neural Information Processing Systems (NIPS) 2007
  • Compare different dimension reduction algorithms (PCA, LLE, ISOMAP, Maximum Variance Unfolding, ...)
    • Nonlinear dimensionality reduction by locally linear embedding.
      Sam Roweis & Lawrence Saul. Science v.290 no.5500, Dec.22, 2000. pp.2323--2326.
    • http://waldron.stanford.edu/~isomap/
    • Unsupervised learning of image manifolds by semidefinite programming K. Q. Weinberger and L. K. Saul (2004). In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR-04), Washington D.C.
  • Compare online and offline algorithms for dimension reduction
    • Random projections
    • Online PCA
  • Implement online clustering algorithm, and apply it to large (e.g., image) data set
    • Online k-Means
  • Implement and evaluate algorithms for online linear/convex optimization (e.g., online shortest paths)
    • A. Kalai, S. Vempala. "Efficient Algorithms for Online Decision Problems" Journal of Computer System Sciences 2005
    • B. McMahan, A. Blum. "Online Geometric Optimization in the Bandit Setting Against an Adaptive Adversary", COLT 2004.

Active learning / experimental design

  • Experiment with heuristics for active learning (e.g., SVMs / logistic regression) on some  interesting data set (e.g., image classification) How much does active learning help?
    • S. Tong, D. Koller. "Support Vector Machine Active Learning with Applications to Text Classification." JMLR 2001.
  • Implement and evaluate some nonstandard active learning algorithm (e.g., [B.3], [B.4], [B.8])
    • S. Dasgupta, D.J. Hsu. "Hierarchical sampling for active learning", ICML 2008.
    • R. Castro, C. Kalish, R. Nowak, R. Qian, T. Rogers, X. Zhu. "Human Active Learning", NIPS 2008.
    • S. Dasgupta, D. Hsu, C. Monteleoni. "A General Agnostic Active Learning Algorithm", NIPS 2007.
  • Use submodular function optimization for Bayesian experimental design on some data set (e.g.,
    • A. Krause, A. Singh, C. Guestrin. "Near-optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies". Journal of Machine Learning Research (JMLR) 2008
    • M. Streeter, D. Golovin "An Online Algorithm for Maximizing Submodular Functions" NIPS 2008
  • Implement batch mode active learning
    • S. Hoi, R. Jin, J. Zhu, M. Lyu "Batch mode active learning and its application to medical image classification", ICML 2006.

Nonparametric learning

  • Compare different active set methods for fast inference in Gaussian Process Regression
    • N. Lawrence, M. Seeger, and R. Herbrich. Fast sparse Gaussian process methods: The informative vector machine. In S. Becker, S. Thrun, and K. Obermayer, editors, Advances in Neural Information Processing Systems 15, pages 609-616, Cambridge, MA, 2003. The MIT Press.
    • Snelson, E. and Ghahramani, Z. (2006) Sparse Gaussian Processes using Pseudo-Inputs.
      In Advances in Neural Information Processing Systems 2005
  • Use GP regression / SVMs for some interesting, nonstandard kernel (e.g., graph kernels, diffusion kernel)
    • Nino Shervashidze, Karsten M. Borgwardt: Fast subtree kernels on graphs, NIPS 2009
    • R. Kondor and J. Lafferty (2002). Diffusion Kernels on Graphs and Other Discrete Input Spaces. ICML 2002
  • Experiment with kernelized algorithms (Kernelized PCA, LDA, ...)
  • Compare Gaussian Process classification with Support Vector Machines on some interesting data set
  • Use Gaussian Processes to predict slip in JPL Mars rover data (contact instructors): Orbital remote sensing imagery of mars to predict areas of high danger to rovers; some of the data is truthed--that is, how much slippage actually occurred during actual rover trajectories.Compare prediction to actual parametric models estimated from slip data.
  • Heteroscedastic GP regression (varying noise)
    • K. Kersting, C. Plagemann, P. Pfaff, W. Burgard. Most-Likely Heteroscedastic Gaussian Process Regression. In Z. Ghahramani, editor(s), Proceedings of the 24th Annual International Conference on Machine Learning (ICML-07)
  • Gaussian process mixture modeling
    • C. E. Rasmussen and Z. Ghahramani. Infinite mixtures of Gaussian process experts. In T. G. Diettrich, S. Becker, and Z. Ghahramani, editors, Advances in Neural Information Processing Systems 14. The MIT Press, 2002.