Spring 2018 Graduate Course Schedule

Full course schedules from the CS Dept or from ODU

Course Descriptions

See Recently Offered Courses for more information on CS 532, CS 541, CS 562, and CS 725


Click the image below for a larger PDF version of the schedule:

CS 495/595 Course Information

Image Processing – Dr. El Mesalami

Description: This course covers different aspects and algorithms for image processing. Topics are pixels operations, image representation, sampling, quantization, frequency representation, spatial and frequency filtering, images sharpening, smoothing, fusion, compression, watermarking, color processing, reconstruction/restoration, and enhancement. Students will write computer programs to implement the material covered in class.

Prerequisites: basic programming, calculus, matrices operations, probability, statistics, differential equations, and basic signal processing knowledge

Advanced Internet of Things (IoT) – Dr. Zhao

Description: This course aims at introducing state-of-the-art IoT solutions and discussing IoT challenges that integrate hardware and software, algorithmic design and architectural development. I will cover such topics as IoT cross-stack programming, smart sensing technology, Internet of web things, low power single chip integration of IoT engines, IoT devices for smart home and wearables, motion planning, gesture recognition, IoT networking, database design for IoT, machine learning and IoT security. Hands-on labs will be planned for enhancing students’ understanding and gaining experience in building secure and intelligent IoT.

Data Science with Python – Dr. Zubair

Description: The objective of this course is to introduce data science using Python programming language. The course will be a hands-on course and requires access to laptops/computers in the class. In the class, lectures will be interleaved with lab work. One of the components of this course is a class project where the students will apply the knowledge acquired during the course for a real application.

A tentative list of topics to cover in this course is given below.

  • Basics of Python programming. Setting up the environment. Use of Python Notebook.
  • Use of libraries like Numpy and Scipy, and Data manipulation using Pandas
  • Computing basic statistics on data
  • Working with higher dimensional data
  • Working with streaming data such as financial data
  • Plotting and visualization
  • Introduction to basic machine learning models

Prerequisites: Elementary Statistics and Probability; Basic programming (any language): variables, data types and expressions, assignment, control-flow statements, functions, arrays, structs/records; and familiarity with Windows and Unix OS.

CS 795/895 Course Information

Pattern Recognition and Molecular Imaging – Dr. He

Description: This course aims at providing hands-on experience working with 3-dimensional molecular images. Students will be exposed to the fundamental concepts and computational techniques that are related to a target project in image pattern recognition. This course is composed of lectures, presentations, discussions, and a project. The project involves the process of understanding the problem to be addressed, the design of the method, and the implementation using a programming language. The goal is to produce a tool at the end of the semester.

Prerequisite: CS 250 or equivalent programming experience