Examples of Course Objectives


For more on course objectives, see, e.g.: http://www.cs.unc.edu/~snoeyink/capcom/Course-Curriculum.htm
and http://ctl.unc.edu/hpl1.html

COMP 122 (Snoeyink)

By the end of the semester each student will:

·         assemble a collection of algorithms and data structures for standard problems such as sorting, dictionary search (static and dynamic), graph traversal and analysis, and optimization,

·         be able to demonstrate that these algorithms are correct using invariants,

·         be able to manipulate recurrences, random variables, and asymptotic notation to analyze running time (worst-case, and sometimes average case) or other resources used,

·         be able to apply the key ideas and algorithmic paradigms (partitioning, balancing, divide and conquer, dynamic programming, greed) to derive variants of the algorithms and data structures for new, but related problems, and

·         be able to choose between alternative solutions by matching the properties of a problem to on the basis of algorithm or data structure properties (e.g. sorting that is stable/unstable or comparison/key-based based on problem size; hash tables vs. red-black trees based on number of dictionary updates), and to explain the choices.

Comp 130 (Hedlund):

This is an introductory course that does not presuppose any previous knowledge of databases. We will study the design and implementation of relational databases. By the end of the semester each student will be able to:


Comp 254, Image Analysis (Pizer):                     


Goal:  To present fundamentals for methods of the analysis of images using a computer, in such a way as to be able to deal with most application problems. Multiscale shape and geometry, statistical pattern recognition, optimization approaches.


Upon completing the course, the student will be able to do the following:

1)      Compute probability distributions on image intensities or objects from training cases.

2)      Use these probabilities for discrimination or estimation by maximum posterior and maximum likelihood methods.

3)      Compute geometric loci such as edges, ridges, and corners.

4)      Use aspects of spatial scale in computing geometric loci and objects’ shape properties.

5)      Form and derive representations of object shape.

6)      Group local image properties into objects.

7)      Choose among optimization methods, esp. for posterior maximization for deformable model segmentation.

1)      Know a catalogue of standard image analysis algorithms and be able to select among these algorithms for new problems.



Comp 5XX: Enabling Technology "Geeks making the world a bit better" (Bishop)
In this course you will:
1) Become familiar with the issues, challenges, and opportunities in using computers to    
     enable people with physical and mental disabilities.
2) Apply your computer skills to develop a solution for a specific disabled user or group.
3) Informally test your solution with users.
4) Write about and make an in-class presentation on your experience and product.