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Computer Systems - COMP
While no outlines currently exist for this course, below are the course learning outcomes/competencies.
Course Learning Outcomes/Competencies
Upon successful completion of this course, the student will be able to:
- Define adaptive, adaptable and hybrid interfaces.
- Describe the decision-making tasks that are included in the application's interface.
- Identify how adaptive interfaces can be used in assistive technologies.
- Describe user models and intelligent interface agents.
- Identify the principles of AI driven UX.
- Analyze and identify user models, user support, socio-organizational issues, and stakeholder requirements of HCI systems.
- Describe various models for combining human and machine intelligence to solve computational problems.
- Identify users' beliefs, intentions and goals for an existing system.
- Design decision models used in user modeling which incorporate users' beliefs, intentions and goals.
- Apply probabilistic models (e.g., the Bayesian theorem) for computing the likelihood of a certain event to happen.
- Discuss the differences between machine learning and predictive modeling.
- Discuss machine learning algorithms used for prediction.
- Apply machine learning algorithms for design of user models and intelligent interface agents.
- In preparation for the Major Project, propose and draft the design of an application that includes (a) user models based on the characteristics of users and tasks and (b) intelligent interface agents that can predict the user's future actions based on the current state of the system, the user's profile, and previous actions and beliefs.
Effective as of Fall 2020