DME – Student Presentations

Date	Slot	UUN	Student	Paper
02/03	1	s1671956   s1679937	Angelos        Eirini	Data Mining for Internet of Things: A Survey
	2	s1686429   s1352385	Richard        George M.	Independent Component Analysis: Algorithms    and Applications
	3	s1046993   s1676828	Josh           Martynas	Random Search for Hyper-Parameter             Optimization
	4	s1110577   s1670546	Bartek         Santiago	Private traits and attributes are predictable from digital records of human behavior
09/03	1	s1667278   s1675946	Chris          Elias	Reducing the Dimensionality of Data with      Neural Networks
	2	s1667813   s1669167	Andreas        Stavros	Isolation forest {for anomaly detection}
	3	s1212549   s1672197	Patrick        Giannis	Meme-tracking and the dynamics of the news    cycle
16/03	1	s1676895   s1687131	Cristina       Alex	Visualizing Data using t-SNE
	2	s1652217   s1604115	Abhinav        Ankush	Probabilistic Principal Component Analysis
	3	s1606815   s1311707	Peter          Manni	On a Connection between Kernel PCA and Metric Multidimensional Scaling
	4	s1142146	Aleksander	Theory-guided Data Science: A New Paradigm    for Scientific Discovery
23/03	1	s1680879   s1671145	Manolis        Lefteris	"Why Should I Trust You?" Explaining the      Predictions of Any Classifier
	2	s1672054   s1685264	Dimitris       Angeliki	BLEU: a method for automatic evaluation of    machine translation
30/03	1	s1687053   s1687568	Christos       George P.	Matrix Factorization Techniques for           Recommender Systems
	2	s1631755   s1666415	Christine      Aisling	Recommender Systems: Missing Data and         Statistical Model Estimation
	3	s1670236   s1682581	Wei Guang      Siyuan	Practical Bayesian Optimization of Machine    Learning Algorithms
	4	s1687620   s1686308	Lasse          Antonio	The Self-Organizing Map

Presentations
The papers are presented by groups of two students. Each group should email the TA the following information by Friday 10 February:

• Names and student numbers
• 3 papers in decreasing order of preference
• Preferred date of the presentation

Please note that we cannot guarantee that we can accommodate everyone's preferences. You can use piazza to find team-mates. If you would like to do the presentations alone rather than in a group, please check with the TA. Please note that individual presentations will only be possible if time slots are available.

• If the presentation is given by two students, both have to contribute equally to the presentation. Both presenters will receive the same grade.
• The presentations last 20 minutes. This will be strictly enforced.
• After each presentation we will have 5 minutes of questions.
• We will typically have 2 presentations per 50 min lecture slot.
• You should prepare slides and send them as a pdf to the lecturer on the day before your presentation. If you prefer another file format for your presentation, please check that the slides project correctly after one of the earlier sessions.

• Research papers typically make a scientific contribution, which means that they propose or claim something that holds and that matters. The overall goal of the presentation is to convey the contribution made in the paper. For that purpose, the presentations should cover:

  1. Very briefly, what is the paper generally about?
  2. Background and/or brief recap of the relevant material from the lecture
  3. What is proposed or claimed in the paper?
  4. What supporting evidence is provided?
  5. Why does the proposal/claim matter?
• Do only include as much mathematics as needed to convey the key message of the paper.
• Feel free to use diagrams and equations from the paper in your slides (with proper acknowledgement).

Summary

• The summary should be structured according to the five highlighted points above.
• In total, the summary of each paper should be maximally half a page. This means one to two sentences per point only. Good diagrams will be helpful.
• Handwritten and scanned documents are ok if legible.
• Please email the summary of two papers per session to the lecturer by Tuesday of the week following the presentation (at noon).

Please feel free to propose papers yourself. Check with the lecturer about suitability.

PCA and its extensions

• Probabilistic Principal Component Analysis
  M. Tipping and C. Bishop
  Journal of the Royal Statistical Society 1999
• Independent Component Analysis: Algorithms and Applications
  A. Hyvarinen
  Neural Networks 2000
• Robust Principal Component Analysis?
  E. Candes, X. Li, Y. Ma, and J. Wright
  Journal of ACM 2009

Dimensionality reduction and data visualisation

• Reducing the Dimensionality of Data with Neural Networks
  G. Hinton and R. Salakhutdinov
  Science 2006
• On a Connection between Kernel PCA and Metric Multidimensional Scaling
  C. Williams
  NIPS 2001
• Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data
  M. Radovanovic et al
  Journal of Machine Learning Research 2010
• Nonlinear Dimensionality Reduction by Locally Linear Embedding ( longer version )
  L. Saul and S. Roweis
  Science 2000
• Visualizing Data using t-SNE
  L. van der Maaten and G. Hinton
  Journal of Machine Learning Research 2008
• The Self-Organizing Map
  T. Kohononen
  Neurocomputing 1998

Performance evaluation, hyperparameter selection

• Image Quality Assessment: From Error Visibility to Structural Similarity
  Z. Wang, A. Bovik, et al
  IEEE Transactions on Image Processing 2004
• BLEU: a Method for Automatic Evaluation of Machine Translation
  K. Papineni, S. Roukos, et al
  Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL) 2002
• Random Search for Hyper-Parameter Optimization
  J. Bergstra and Y. Bengio
  Journal of Machine Learning Research
• Practical Bayesian Optimization of Machine Learning Algorithms
  J. Snoek, H. Larochelle, and R. Adams
  NIPS 2012
• "Why Should I Trust You?" Explaining the Predictions of Any Classifier
  M.T. Ribeiro, S. Singh, and C. Guestrin
  Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Missing data, outliers, and anomaly detection

• Isolation forest {for anomaly detection}
  Liu et al
  Eighth IEEE International Conference on Data Mining 2008
• Removing Electroencephalographic Artifacts: Comparison between ICA and PCA
  T.P. Jung et al
  Proceedings of the 1998 IEEE Signal Processing Society Workshop 1998
• Recommender Systems: Missing Data and Statistical Model Estimation
  B. Marlin et al
  Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence (IJCAI 2011)
• LOF: Identifying Density-Based Local Outliers
  M. Breunig et al
  Proceedings of the ACM SIGMOD International Conference on Management of Data 2000
• Support Vector Data Description
  D. Tax and R. Duin
  Machine Learning 2004
• Anomaly Detection: A Survey
  V. Chandola, A. Banerjee, and V. Kumar ACM Computing Surveys (CSUR), 2009

Miscellaneous

• Private traits and attributes are predictable from digital records of human behavior
  M. Kosinski et al
  Proceedings of the National Academy of Sciences 2013
• Learning Fair Representations
  R. Zemel et al
  Proceedings of the 30th International Conference on Machine Learning 2013
• Data Mining for Internet of Things: A Survey
  C.-W. Tsai, C.-F. Lai, and M.-C. Chiang
  IEEE Communications Surveys & Tutorials, 16(1) 2014
• Hidden Technical Debt in Machine Learning Systems
  D. Sculley et al
  Advances in Neural Information Processing Systems 28, 2015
• Meme-tracking and the dynamics of the news cycle
  J. Leskovec, L. Backstrom, and J. Kleinberg
  Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining 2009
• Theory-guided Data Science: A New Paradigm for Scientific Discovery
  A. Karpatne et al
  (paper under review)
• Matrix Factorization Techniques for Recommender Systems
  Y. Koren, R. Bell, and C. Volinsky
  Computer, 42(8), 2009