Instructor: Ivo D. Dinov, Ph.D. Assistant Professor in Statistics, Research Scientist, Department of Neurology, UCLA School of Medicine E-mail:

Teaching Assistants: TBA E-mail: tba@stat.ucla.edu

Lectures: PAB 2748 ,  Tue & Thu  8:00 - 9:15 AM

Office Hours
• Dr. Dinov: by appointment
• TA's:  TBA  TBA
• VOH/Forum 24/7

Tentative schedule of presentations:
• Tenth week of classes: PAB 2748,  Tue & Thu  8:00 - 9:15 AM

Tentative schedule of topics to be covered
1. Review (2 wks):
       - Computer Systems, Writing, compiling, making, packaging,
           distributing and running programs/software
       - Variables and assignments, Input/Output, Data types and
           expressions
            - Procedural (structured) vs. object oriented programming
       - Classes, methods, abstract data types
       - Overloading (functions & classes)
       - Call-by-value vs. call-by-reference
       - I/O Streams
       - Multidimensional Arrays
       - Strings
       - Pointers, dynamic arrays
       - Recursion
2. Template (classes and methods) (1 wk)
       - Algorithm and Data abstraction
       HW1: Create a simple software package that reads in an image,
           blurs the image and saves out the result.
3. Linked lists, graphs (trees) (1 wk)
       - Traversing
       - Sorting
4. Visualization ToolKit & Java interfaces, (1 wks) * R/C++ Integration
   
1. JNI - Java C/C++ interface for computational/visualization interaction

2. makefiles, documentation, packaging

3. HW2: Create a GUI that reads in a volume (or a series of images) processes these by temporal (index) moving average, displays the original and the filtered volume and saves the latter out.
5. Inheritance (1 wk)
       - Constructors, derived classes, polymorphisms
6. Exception handling mechanisms (1 wk)
       HW3: Extend and add more processing tools, more stability (incl. exception handling) and more portability to the previous package.
7. Statistical Computing (2 wks)
       - Data stat summaries
       - model fitting
       - others
8. Data Filtering (2 wks)
       HW4: Extend and add a variety of statistical analysis tools to your previous package (e.g. intensity-based image segmentation, wavelet shrinkage, Fourier decomposition).

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