Postgraduate Courses
CSIC
Scientific Computation
- CSIC 5011Topological and Geometric Data Reduction and Visualization[3-0-0:3]Co-list withMATH 5473Exclusion(s)MATH 5473DescriptionThis course is a mathematical introduction to data analysis and visualization with a perspective of topology and geometry. Topics covered include: classical linear dimensionality reduction, the principal component analysis (PCA) and its dual multidimensional scaling (MDS), as well as extensions to manifold learning, topological data analysis, and sparse models in applied math/high dimensional statistics. Extensive application examples in biology, finance, and information technology are presented along with course projects.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Explain the basics concept of dimensional reduction.
- 2.Use Compressed Sensing and High Dimensional Statistics techniques for data analysis.
- 3.Use Maniford learning approach for applications
- 4.Apply the data analysis method in applications
- CSIC 5031Modeling, Optimization, and Statistics[3-0-0:3]DescriptionThis course will cover modeling using optimization, optimization theory and techniques, and their applications in statistics related topics. In particular, it includes basic convex analysis theory, basics convex optimization algorithms, optimization for statistical regression and classification, optimization for signal/image processing and machine learning.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Model a read world problem as an optimization problem.
- 2.Apply the basic theory of convex optimization.
- 3.Design basic numerical algorithms for the optimization problem.
- 4.Solve statistics/machine learning problems by optimization techniques.
- CSIC 5190Special Topics in Scientific Computation[2-0-0:2]DescriptionThis course will introduce advanced topics of current interests in scientific computing to PG students. May be graded by letter, P/F for different offerings.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Develop research topics which are in line with the current developments and trend in the area of scientific computations.
- 2.Recall and be acquainted with current literature in the topic area.
