The University of Texas at Austin 3 credits Computer Science
Explore ways in which contemporary machine learning (ML) algorithms can be untrustworthy, along with algorithmic approaches to mitigate these trust issues. Discuss algorithmic fairness, privacy in ML, ML security, and interpretability/explicability in ML. Examine fairness, security, and privacy issues in ML. Only one of the following may be counted: Computer Science 365T, 378 (Topic: Trustworthy Machine Learning), 378 (Topic: Safe and Ethical AI). Three lecture hours a week for one semester. Pre/Corequisites: Computer Science 331 or 331H with a grade of a least C-.
Our subject experts help with essays, research papers, case studies, discussion posts, and capstone projects for these programs β written from scratch to your course requirements.
Program and course information is reproduced from each university's published academic catalog for informational purposes and may change. Always confirm current requirements with your university. We are an independent academic support service and are not affiliated with or endorsed by the universities listed.
Expert guides on essay writing, citation styles, study skills, and academic success.
View All Articles
Learn how to write a good research question for a paper. Narrow your topic, identify a research problem, and create a focused question with practical examples.
Learn how to choose a research topic you can actually research. Discover how to narrow broad ideas, find reliable sources, and develop a focused research question.
Learn how to cite sources in a research paper using APA, MLA, and Chicago styles, with in-text citation examples and reference-list tips.