
The following courses are provided by the Department of Computer Science and Engineering and the University of South Carolina for training in machine learning, data mining, bioinformatics and mathematics. Fall 2012: CSCE822 Data Mining
Spring 2012: CSCE883 Machine Learning
Fall 2011: CSCE240 Introduction to Software Engineering (c++)
Spring 2011: CSCE569 Parallel Programming
Fall 2010: CSCE822 Data Mining
Spring 2010: CSCE883 Machine LearningThis graduate level machine learning course will cover most topics in techniques and algorithms involved in clustering, classifiation, dimension reduction, kernel methods, HMM, Bayesian methods, graphical models, reinforcement learning. We are use realworld examples to illustrate the principles and ideas. Machine learning techniques are underlying the operations of many great services we used everyday such as Google search engine, Amazon shopping store, iPhone app voice recognition, and etc. Fall 2009: CSCE350 Algorithms and Data structuresThis undergraduate level algorithm course will cover most topics in algorithm design and analysis techniques. We have covered many of the classical smart algorithms such as Quicksort, heap sort, MST, Dijiska algorithm, and etc. We are use realworld examples to illustrate the principles and ideas. Spring 2009: CSCE569 Parallel ComputingThis undergraduate level course will cover most topics in techniques and algorithms involved indeveloping parallel programs that can run on multicpu cluster computers. Parallel computing is critical for many applications in the area of bioinformatics, data mining, and scientific computation. The main platform is MPI. Fall 2008: CSCE822 Data Mining and WarehousingThis graduate level data mining course will cover most topics in techniques and algorithms involved in clustering, classifiation, frequent itemset mining, sequential mining, text mining. We are use realworld examples to illustrate the principles and ideas. Spring 2008: CSCE555 Algorithms in BioinformaticsThis is a course for undergraduates and junior graduate students with first exposure to bioinformatics. Topics include DNA sequence analysis, Microarray data analysis, protein structure analysis.. Fall 2007: CSCE822 Data Mining and WarehousingThis graduate level data mining course will cover most topics in techniques and algorithms involved in clustering, classifiation, frequent itemset mining, sequential mining, text mining. We are use realworld examples to illustrate the principles and ideas. CSCE580: Artificial Intelligence
Main topics of traditional logicbased artificial intelligence.

