K12 Education
Courses
What is Bioinformatics?
Machine learning
Evolutionary Computation

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.


CSCE768 Pattern Recognition

This course will cover the techniques and topics that are widely used in real-world pattern recognition. It will prepare you with skills for working in companies such as Google, Microsoft, Amazon, IBM, and many other business intelligence enterprises. Pattern recognition ( along with Machine learning or data mining) is at the center of information revolution.

CSCE206 Scientific Application Programming Data (using Python)

This course aims to help learn scientific programming skills using Python, which is the most popular programming course. It is easy to learn while is very powerful to get job done. Your productivity with Python would be several times higher than thoses who only knows java or C/C++ programming. It is also the main programming language used in my research lab. Recommended to all students.

CSCE590 Mobile Application Development

This course introduces the programming skills for developing hybrid mobile apps that run across all major platforms such as ios, android, windows, and etc. It uses HTML5 and ionic framework to develop the apps. Required skills include Javascript, HTML. Availabe for both undergraduates and gradaute students.

CSCE240 Introduction to Software Engineering (c++)

CSCE569 Parallel Programming

This undergraduate level course will cover most topics in techniques and algorithms involved indeveloping parallel programs that can run on multi-cpu cluster computers. Parallel computing is critical for many applications in the area of bioinformatics, data mining, and scientific computation. The main platform is MPI

CSCE822 Data Mining

This 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 real-world examples to illustrate the principles and ideas.

CSCE883 Machine Learning

This 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 real-world 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.

CSCE350 Algorithms and Data structures

This 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 real-world examples to illustrate the principles and ideas.

CSCE822 Data Mining and Warehousing

This 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 real-world examples to illustrate the principles and ideas.

CSCE555 Algorithms in Bioinformatics

This 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..




CSCE580: Artificial Intelligence

Main topics of traditional logic-based artificial intelligence.