Course Information: Machine Learning and BioInformatics

Computer Science Department
Course Competency Plan
COURSE: CpSc 480 - Machine Learning and BioInformatics

Course Description: The in-depth examination of a specific topic. For different topics, this course may be repeated for required elective credits toward a computer science major or minor. (3 credits)

Course Outcomes: This course and its outcomes support the Information Technology Learning Outcomes of Problem Solving and Critical Thinking (PS&CT) and Ethical and Professional Responsibilities (E&PR). These Information Technology Learning Outcomes are tied directly to the University Wide Outcomes of Critical Thinking and Problem Solving, and Values and Ethics.

Degree

Program Objective

Assessed Course Objective

CS

I.c. Examine and analyze alternative solutions to a problem

 1. Explain what machine learning and robotics mean and how machines can be made to process information intelligently and perform physical human tasks. 

CS

I.d. Develop abstract models to simulate complex systems.

CS

I.e. Determine appropriate hardware and software combinations for maximum efficiency.

CS

I.f. Determine correctness and efficiency of a system design and implementation

CS

I.c. Examine and analyze alternative solutions to a problem

 2. Describe different machine learning methods such as neural networks, expert systems, genetic algorithms and other machine learning paradigms 

CS

I.d.Develop abstract models to simulate complex systems.

CS

I.e. Determine appropriate hardware and software combinations for maximum efficiency.

CS

I.f. Determine correctness and efficiency of a system design and implementation

CS

I.b. Implement an algorithm by creating a tested and debugged programmatic solution

 3. Write computer programs and/or use shell programs that solve problems intelligently.

CS

II.a. Document all aspects of a system precisely and clearly.

CS

I.b. Implement an algorithm by creating a tested and debugged programmatic solution

 4. Write programs for robotic hardware

CS

II.a. Document all aspects of a system precisely and clearly.