Ivo Dinov
UCLA Statistics, Neurology, LONI
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Statistics 130D
Winter 2004

Statistical Programming, Computation, and Visualization in C/C++



Course Description

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: Haines 25A  MWF  9:00 - 9:50 AM


    Instructor Office: Main: MS 8142E (alternative: CHS, UCLA School of Medicine, Reed 4-238, by appt. only)
    TA Offices:  TBA , MS TBA
    Virtual Office Hours (STAT130D Forum)
    STAT Computer Lab: http://www.stat.ucla.edu/undergraduate/icl/

    Grading policy and basis for Final Grade:
    Project Assignment Policy:

    Textbook: Class notes + textbooks to be announced
      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)
      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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