EC ENGR 203A
Human Factors in Artificial Intelligence
Electrical and Computer Engineering · 4 units · Graduate courses (200-299)
Examination of how humans influence and are influenced by AI across its lifecycle—from data collection and annotation to model development and deployment. Topics include fairness and bias, trust and reliance, interpretability, and interactivity. Emphasis on human-centered design, ethics, and societal impact. Through lectures and projects, including creative formats such as debates on human–AI interaction, students develop the skills to design and evaluate AI systems that are both technically sound and user-centered.
Letter grading.
When it runs
Checking the Schedule of Classes…
Requisites
Official UCLA wording
Preparation: familiarity with algorithms such as linear regression, decision trees, clustering, and neural networks; competence in probability distributions, statistical inference, and hypothesis testing; foundational knowledge of modern artificial intelligence/machine learning (AI/ML) comparable to an introductory undergraduate course; and proficiency in Python, including libraries such as NumPy, pandas, and scikit-learn.
BruinTree reads · Preparation
needs reviewconfidence 0.00 · from textBruinTree could not read this requirement — see UCLA’s wording above.
- · could not read "familiarity with algorithms such as linear regression" (no course number found)
- · could not read "decision trees" (no course number found)
- · could not read "clustering" (no course number found)
- · could not read "neural networks" (no course number found)
- · could not read "competence in probability distributions" (no course number found)
- · could not read "statistical inference" (no course number found)
- · could not read "hypothesis testing" (no course number found)
- · could not read "foundational knowledge of modern artificial intelligence/machine learning (AI/ML) comparable to an introductory undergraduate course" (no course number found)
- · could not read "proficiency in Python" (no course number found)
- · could not read "including libraries such as NumPy" (no course number found)
- · could not read "pandas" (no course number found)
- · could not read "scikit-learn" (no course number found)
- · no course reference could be read from this requisite
Requires
Everything that has to come before this course, not just the courses named in the requisite above.
Nothing — this is an entry point.
Unlocks
What this course is a requisite for, and what those courses lead to in turn.
No course in the catalog lists this as a requisite.