MLBio+Laboratory Machine Learning in Biomedical Informatics



Teaching

I am the 2014 GMU Teaching Excellence Award Winner. My portfolio is here

I like teaching classes a lot. I have had formal training in active learning strategies for course preparation. Some of the classes that I have taught before or am currently going to teach are under the classes section.
Classes I have taught or will teach in the future:

CS 465: Computer Systems Architecture (Fall 2010, Fall 2012)
This course provides an introduction to the fundamental concepts in computer architecture. Topics include: Basic system components, Performance measurements, Instructions and their representation, Number representation, Implementation of Arithmetic operations, Processor organization, Pipelining, The memory hierarchy
CS 795 Special Topics: Biological Data Mining (Fall 2009)
CS 795 (Biological Data Mining) is a seminar-based class where students learn about the state-of-the-art machine learning methods and their applications in bioinformatics & computational biology. The course will cover important concepts in supervised, unsupervised, and semi-supervised learning and then zoom to specific problems in biology. The material will be covered using a series of research papers published in reputed journals. Example applications include: fold recognition, secondary structure prediction, genome annotation, network inference, protein-protein interaction, and metagenomics.
CS 795 Special Topics: Biological Sequence Analysis (Spring 2009, Spring 2010)
CS 795 (Biological Sequence Analysis) is an inter-disciplinary course aimed at bridging the gap between biology and computer science, by exposing students to the widely used algorithms and methods playing a key role in bioinformatics and computational biology. The human genome project and advances in sequencing technologies have left us with a wealth of DNA, RNA, protein sequence data. Its important to infer key characteristics of biological systems using sequence analysis methods. The first half of the course will help students understand basic sequence alignment algorithms, hidden Markov models, classification and prediction methods. The second half will be an application of the concepts and ideas learned to some of the current bioinformatics applications motivated with a fair biological understanding.
INFS 755: Data Mining at George Mason University (Fall 2008)
Over the past decade there has been an exponential increase in the amount of data. This has lead to development of techniques to discover useful and interesting information from the large collections of data. This course aims to provide a overview of the key data mining methods and techniques like classification, clustering, and association rule mining. The course will also provide interesting application examples of data mining, especially in the field of bioinformatics and spatial data mining.
CSCI 5481: Computational Methods for Genomics at the University of Minnesota (Fall 2006)
Csci 5481 is an inter-disciplinary course aimed at bridging the gap between biology and computer science, by exposing students to the widely used algorithms and methods playing a key role in bioinformatics and computational biology. The first half of the course will help students understand basic sequence alignment algorithms, hidden Markov models, classification and prediction methods. The second half will be an application of the concepts and ideas learned to some of the current bioinformatics applications motivated with a fair biological understanding.


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