CIS734: Introduction to Genomics and Bioinformatics (4 credits)

The course provides fundamental background in bioinformatics, both theoretical (bioinformatics algorithms) and practical (databases and web-based tools used to study problems in biology), to students in computer science or in biological sciences. It is designed for both undergrad and graduate students. The requirements for the undergrad students are a strict subset of the requirements for graduate students. Introduction to the biological problems addressed in this course will be provided, together with a formal definition of the computational problems and a deep exploration of the algorithms for solving these problems. Practical use of the tools introduced in class will be demonstrated by laboratory exercises and homework problems. Students will be grouped for class assignments, such that each group will contain at least one life scientist and one computer scientist. Course projects will be related to ongoing biological research projects at KSU.
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CIS890: Machine Learning for Bioinformatics (3 credits)

Advances in high-throughput experiments and sequencing technologies have resulted in large amounts of data in biological sciences. This has led to unprecedented opportunities for large-scale knowledge discovery in a number of areas, including characterization of macromolecular sequence-structure-function relationships and discovery of complex genetic regulatory networks, among others. Machine learning algorithms offer some of the most cost-effective approaches to automated knowledge discovery in emerging data-rich disciplines. In this course, some of the most important machine learning algorithms and their applications to bioinformatics are discussed.
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CIS833: Information Retrieval and Text Mining (3 credits)

Information Retrieval (IR) refers to the processing, indexing and querying of unstructured or loosely structured information. This course will focus on the theory and practice of search engines for retrieving textual information (including web documents). Basic and advanced topics in IR will be covered, with emphasis on newer technologies that go beyond simple keyword search. Programming assignments will provide hands-on experience with retrieval systems. More advanced research in IR will be stimulated through the means of a class project.
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CIS560: Database System Concepts (3 credits)

The purpose of this course is to introduce concepts, approaches, and techniques in database management. This includes exploring the representation of information as data, data storage techniques, foundations of logical data models, data retrieval, database design, transaction management, integrity and security.
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CIS890: Top/Deep Learning (3 credits)

Deep learning is a fast-growing area in Machine Learning. By using complex neural networks with many layers, deep learning has revolutionized many application domains equipped with vaste amounts of data for training the networks. Sample domains where deep learning has led to groundbreaking results include: computer vision, language processing, speech recognition, machine translation, autonomous driving, etc. In this course, we will cover the foundations of deep learning, with focus on different types of modern neural network (such as convolutional neural networks, recurrent and recursive neural networks, long short-term memory networks, autoencoders). Furthermore, we will also cover tools and software available for building and training deep neural networks. Basic knowledge in machine learning is expected.
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For recent offerings of these courses, please visit the ML&DS website.