Current Courses

Teaching Philosophy

Courses I Teach

• CS 5955 // CS-6955 - Data Mining   (http://www.cs.utah.edu/~jeffp/teaching/cs5955.html)
  Data mining is the study of efficiently finding structures and patterns in data sets. We also study what structures and patterns you can not find. The structure and patterns are based on statistical and probabilistic principals, and they are found efficiently through the use of clevel algorithms. This class will take this two-pronged approach to the topic; we will understand the model and then explore efficient algorithms to find them. Topics will include: similarity search, clustering, regression/dimensionality reduction, anomaly detection, link analysis (PageRank), and small space summaries.
• CS 7941 - Data Mining Seminar   (http://www.cs.utah.edu/~jeffp/teaching/cs7941.html)
  Enrolled students will hear and present cutting edge research in data mining, databases, and algorithms.
• CS 7960 - Models of Computation for Massive Data   (http://www.cs.utah.edu/~jeffp/teaching/cs7960.html)
  This course explores advanced models of computation pertinent for processing massive data sets. As data sets grow to terabyte and petabyte scales, traditional models and paradigms of sequential computation become obsolete. Different efficiency trade-offs analyzing memory usage, I/O calls, or inter-node communication become the dominant bottlenecks. These paradigms are formalized as I/O-Efficient, Parallel, Streaming, GPU-based, Map-Reduce, and other distributed algorithmic models of computation. This course will study the history and specifics of these models. Students in the class learn the proper settings in which to use these paradigms, the advantages and disadvantages of each model, and how to analyze algorithms with these settings