Course Outcomes for CSC 422 - Automated Learning and Data Analysis

Upon successful completion of this course, a student will be able to...

  1. List and explain the major types of data and data representations;
  2. List and explain the problems arising in preparing data for analysis, and the methods for addressing these problems;
  3. List and explain representative applications of automated learning and data analysis;
  4. List and explain representative benefits and dangers of automated learning and data analysis;
  5. Identify some ethical issues in data analysis applications;
  6. List and explain the fundamental roles of knowledge in action;
  7. Explain the iterative process of formulating knowledge;
  8. List and explain the fundamental properties of formulations of knowledge and their use in evaluating and criticizing formulations;
  9. List and explain some principal representations of knowledge, and compare their strengths and weakness for different representational tasks;
  10. List, explain, and apply the major knowledge discovery techniques;
  11. Compare the strengths, weaknesses, and prerequisites of automated learning techniques;
  12. Design a detailed plan of analysis for a realistic data set;
  13. Identify contingencies occuring in a data analysis;
  14. Apply automated data analysis tools to carry out a data analysis plan; and
  15. Motivate, justify, and qualify conclusions obtained from an analysis.

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