Machine learning has driven remarkable advances in many fields and, recently, it has been pivotal in enhancing the diagnosis and treatment of complex brain disorders. Biomedical and neuroscience studies frequently rely on neuroimaging as it provides non-invasive quantitative measurement of the structure and function of the nervous system. Machine and deep learning methods can, for example, refine findings for specific diseases or cohorts enabling the detection of imaging markers at an individual level. This, in turn, paves the way for personalized treatment plans. In this course, we explore the methodological gaps in analyzing high-dimensional, longitudinal, and heterogeneous neuroimaging data and study novel, robust, scalable, and interpretable machine learning models for this purpose.Students have the option to enroll in the class for either 3 or 4 units. All students, regardless of their unit choice, are expected to attend every class session. The primary class content will cover the fundamentals of machine learning, offer some limited hands-on training, and explore the application of ML to neuroimaging. Those opting for 4 units will benefit from an extra hour of instruction weekly, diving deeper into core ML concepts and receiving extended hands-on training. The scheduling of this additional hour will be determined based on the availability of the students enrolled for 4 units to ensure a mutually convenient time slot. Undergraduate students and those who do not have ML backgrounds are advised to take the course for 4 units.
3-4 units · Medical Option (Med-Ltr-CR/NC)
Machine learning has driven remarkable advances in many fields and, recently, it has been pivotal in enhancing the diagnosis and treatment of complex brain disorders. Biomedical and neuroscience studies frequently rely on neuroimaging as it provides non-invasive quantitative measurement of the structure and function of the nervous system. Machine and deep learning methods can, for example, refine findings for specific diseases or cohorts enabling the detection of imaging markers at an individual level. This, in turn, paves the way for personalized treatment plans. In this course, we explore the methodological gaps in analyzing high-dimensional, longitudinal, and heterogeneous neuroimaging data and study novel, robust, scalable, and interpretable machine learning models for this purpose.Students have the option to enroll in the class for either 3 or 4 units. All students, regardless of their unit choice, are expected to attend every class session. The primary class content will cover the fundamentals of machine learning, offer some limited hands-on training, and explore the application of ML to neuroimaging. Those opting for 4 units will benefit from an extra hour of instruction weekly, diving deeper into core ML concepts and receiving extended hands-on training. The scheduling of this additional hour will be determined based on the availability of the students enrolled for 4 units to ensure a mutually convenient time slot. Undergraduate students and those who do not have ML backgrounds are advised to take the course for 4 units.
Offered in Winter 2027 at Stanford University.