PREREQUISITES:
MAS students: Completion of the MAS Core courses, plus permission of the instructor.
MLIS and Dual students: Some electives can be taken in conjunction with the MLIS Core courses; consult with the MLIS Program Chair for recommendations.
All students: Prior computer programming experience is required for any student taking this course.
GOAL: This course aims to provide you with the knowledge, skills, and ethical understanding needed to develop and evaluate machine learning models. Through a blend of theoretical study, hands-on exercises, and critical discussions, you will learn to integrate machine learning models with principles of human-centered design. By the end of the course, you will understand how AI technologies are developed and evaluated, and you will be able to engage in discussions on the responsible development of AI, ethical concerns, the human aspects of designing AI, and societal impacts.
OBJECTIVES:
Upon completion of this course, you will be able to:
- Gain a foundational understanding of supervised and unsupervised machine learning techniques.
- Analyze the strengths and limitations of various machine learning algorithms for specific tasks.
- Critically assess the ethical implications of AI development and deployment.
- Develop communication skills to effectively discuss technical concepts.
- Collaborate with your peers on a project to implement a machine learning technique using a provided dataset.
CONTENT:
- Introduction to AI and Machine Learning
- Supervised Learning
- Deep Learning
- Unsupervised Learning
- Evaluation of Machine Learning Models
- Human Aspects of Designing AI Systems
- Explainable AI
- Algorithmic Fairness
- Human-AI Interaction
- Privacy and Security in AI
- Societal Impacts of AI