Data Science (DATASCI)

DATASCI 225  Advanced Machine Learning for the Biomedical Sciences II  (3 Units)  Spring  

Instructor(s): Gilmer Valdes

Prerequisite(s): Biostats 213 or equivalent. Biostat 216 and Biostat 208 or equivalent

Restrictions: This course is part of the Training in Clinical Research (TICR) Program and may have space limitations. Auditing is not permited.

Activities: Lecture, Seminar, Clinical, Fieldwork, Independent Study, Project, Web work, Workshop, Practical Experience, Special Projects, Lab skills, Lab science, Conference, Discussion

This course covers the underlying formulation of machine learning algorithms. Its focus is on providing deep understanding of machine learning methodology. This is an advanced course in machine learning and its objective is to provide students with a strong foundation so that they can properly manipulate and customize black box machine learning library packages. Students will implement popular machine learning algorithms and customize them to best satisfy specific needs in medicine.

View full course details:

  • School: Graduate Division
  • Department: Clinical Research Program
  • May the student choose the instructor for this course? No
  • Does enrollment in this course require instructor approval? No
  • Course Grading Convention: Letter Grade, P/NP (Pass/Not Pass) or S/U (Satisfactory/Unsatisfactory)
  • Graduate Division course: Yes
  • Is this a web-based online course? Yes
  • Is this an Interprofessional Education (IPE) course? No
  • May students in the Graduate Division (i.e. pursuing Master or PhD) enroll in this course? Yes