Course Schedule - Fall Semester 2026

     

Meeting location information can now be found on student schedules in ESTHER (for students) or on the Course Roster in ESTHER (for faculty and instructors).
Additional information available here.

ANTH 313 001 (CRN: 16110)

AI AND HUMAN ORIGINS

Long Title: DEEP EVOLUTION: USING ARTIFICIAL INTELLIGENCE TO DECODE EARLY HUMAN ORIGINS
Department: Anthropology
Instructor: Dominguez Rodrigo, Manuel
Meeting: 10:50AM - 12:05PM TR (24-AUG-2026 - 4-DEC-2026) 
Part of Term: Full Term
Grade Mode: Standard Letter
Course Type: Lecture/Laboratory
Language of Instruction: Taught in English
Method of Instruction: Face to Face
Credit Hours: 3
Course Syllabus:
Course Materials: Rice Campus Store
 
Restrictions:
Must be enrolled in one of the following Level(s):
Undergraduate Professional
Visiting Undergraduate
Undergraduate
Prerequisites: ANTH 203
Section Max Enrollment: 15
Section Enrolled: 3
Waitlisted: 0 (Max 99) 
Current members of the waitlist have priority for available seats.
Enrollment data as of: 26-APR-2026 10:39AM
 
Additional Fees: None
 
Final Exam: No Final Exam
 
Description: This course explores how artificial intelligence, machine learning, and data science are reshaping the study of human evolution by integrating computational approaches with paleoanthropology, anatomy, and archaeology. Through a chronological journey from the earliest hominins to the emergence of early humans, students learn how algorithms can classify fossils, detect morphological and behavioral patterns, reconstruct evolutionary relationships, and simulate the ecological and adaptive processes that shaped our species. Using real paleontological and archaeological datasets (3D bones and fossils), the course shows how AI enables new perspectives on long-standing questions about the origins of hominins (up to the emergence of Homo), the evolution of cognition and culture, and the environmental pressures driving evolutionary change. Emphasizing a multidisciplinary framework that combines biological evidence with computer vision and evolutionary modeling, the course equips students to critically assess the role of AI in evolutionary science and to apply digital tools to their own research questions, all in an accessible format that requires no prior background in mathematics or coding. Mutually Exclusive: Cannot register for ANTH 313 if student has credit for ANTH 513.