| Syllabus
COS 598D: Data Analysis and Modeling in Science, Engineering and Information Services
Mondays 1:30 pm – 4:00 pm
Computer Science Room 105 (Small Auditorium)
Course Professor: Jaswinder Pal Singh
Coordinator: Steven Kleinstein
TA: Christopher Calderon
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| Monday, February 6: PCA, SVD and their applications (JP Singh)
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| Reading Material 1 | ||
| Reading Material 2 | ||
| Reading Material 3 | ||
| Reading Material 4 | ||
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| Monday, February 13: Clustering with Applications
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Mixture models, k-means, hierarchical, spectral (Charikar)
Applications of PCA/SVD and Clustering (Charikar and Ned Wingreen)
Slides | ||
| Reading Material | ||
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| Monday, February 20: Advanced Dimensionality Reduction
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Random projections and Sketches (Charikar) Applications (Funkhouser) A. Jain. M.N. Murthy, P.J.Flynn. "Data Clustering: A Review". ACM Computing Surveys, Vol. 31, No. 3, September 1999 P. Berkhin. "Survey of clustering data mining techniques". Technical report, Accrue Software, 2002.Rui Xu; Wunsch, D., II, "Survey of clustering algorithms," Neural Networks, IEEE Transactions on , vol.16, no.3pp. 645- 678, May 2005 | ||
| Monday, February 27: Classification (Schapire)
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SVM, boosting, logistic regression, etc.
Applications (e.g., text classification)
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| Reading Material 1 | ||
| Reading Material 2 | ||
| Reading Material 3 | ||
| Slides | ||
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| Monday, March 6: Graphical Models I:
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Introduction to Bayesian networks; Parameter learning and inference (Troyanskaya)
Introduction to Hidden Markov Models (M. Singh)
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| Reading Material | ||
| Rabiner and Juang. An Introduction to Hidden Markov Models IEEE ASSP Magazine 3(1): 4--16. January 1986 | ||
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| Monday, March 13: Graphical Models II:
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Probabilistic approaches to dimensionality reduction (Blei)
*****Variational methods for approximate inference
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| Reading Material 1 | ||
| Reading Material 2 | ||
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Transcription factors example for Bayesian networks (Tavazoie)
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| Reading Material | ||
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| Monday, March 27: Model-based data-analysis I (Shvartsman)
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ODE-based models
Parameter estimation, confidence intervals
Application example: model-based mechanism discrimination for EGF receptor trafficking
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| Monday, April 3: Model-based data-analysisII (Shvartsman)
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PDE-based models
Dimensionality reduction (nondimensionalization and scaling)
Parameter estimation
Application example: pattern formation in developing tissues
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| Monday, April 10: Data Dimensionality Reduction for Dynamic Systems I (Kevrekidis)
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Spectral methods
Manifold learning techniques
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| Monday, April 17: Data Dimensionality Reduction for Dynamic Systems II (Kevrekidis)
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Identification of slow variables and dynamically meaningful reaction coordinates
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| Monday, April 24: Model Dimensionality Reduction (Li)
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| Monday, May 1: Scalable Computing (JP Singh)
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