Course Description
Advanced topics in data analysis, with an emphasis on statistical learning and related optimization problems. The applications include regression, classification, and other tasks in image analysis. The lectures will be based on books and articles in the field of computer vision and medical image analysis.
Athena Title
Advanced Data Analytics
Prerequisite
CSCI 4150/6150 or CSCI 4380/6380 or permission of department.
Semester Course Offered
Not offered on a regular basis.
Grading System
A - F (Traditional)
Course Objectives
After completion of this course, students will be able to: 1. Become familiar with some advanced learning algorithms and techniques and their applications. 2. Understand methodology and use tools to apply learning algorithms to real data and evaluate their performance. 3. Develop programs to solve classification, regression, reconstruction, and segmentation tasks. 4. Understand the theory of optimization methods and describe solution methods in optimization. 5. Determine when an appropriate optimization technique should be selected and apply optimization techniques in problems of image analysis.
Topical Outline
1. Image and shape regression for cross-sectional and longitudinal data. 2. Sparse representation, dictionary learning, and low-rank approximation. 3. Random forests: classification forests and regression forests. 4. Deep learning: convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Autoencoders.