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Master of Public Health (MPH) (S55)  (Dept. Info)Social Work and Public Health  (Policies)FL2024
S55 MPH 5601Applied Deep Learning Using Health Data3.0 Units
Description:Data are now available to social scientists in a way and quantity that has never existed before, presenting unprecedented opportunities for advancing social research and practices through state-of-the-art data analytics. On the other hand, dealing with extensive, complex, unconventional "big data" (e.g., free text, image, video/audio recording) requires revolutionary analytic tools only made available during the past decade. Artificial intelligence (AI), characterized by machine and deep learning, has become increasingly recognized as an indispensable tool in modern social and behavioral sciences. For example, AI methodologies have been applied to enhance the effectiveness of diagnosis and prediction of disease conditions, advance understanding of human development and functioning, and improve the effectiveness of data management in various social and human services. As a subdomain of AI, deep learning is based on artificial neural networks in which multiple ("deep") layers of processing are used to extract higher-level features progressively from data. This layered representation enables modeling more complex, dynamic patterns than the traditional machine learning (which sometimes are called "shallow learning" as in contrast to deep learning), which finds its utility in analyzing the "big data"-data massive in scale and "messy" to work with (e.g., unstructured texts, images, audios, and videos). This course contributes to the overarching goal of training next-generation researchers in modern data analytics. It aims to equip students with the core knowledge and essential skills to apply deep learning models to address real-world problems. Through the course, students will familiarize themselves with computer programming in data science, learn state-of-the-art deep learning models, and apply them to social and behavioral questions. In addition, one essential field of deep learning applications is assisting decision-making through identifying patterns and trends, improving prediction precision, and automating evidence collection, synthetization, and dissemination. Students who master deep learning tools will be at the frontier to leverage the power of AI in analytics and practices.
Attributes:
Instruction Type:Classroom instruction Grade Options:C Fees:
Course Type:HomeSame As:S90 5601Frequency:Every Semester / History
SecDays       TimeBuilding / RoomInstructorFinal ExamSeatsEnrollWaits
01-T-----9:00A-12:00PTBAAnDefault - none20150
Actions:BooksSyllabus
Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use.
Label

Home/Ident

A course may be either a “Home” course or an “Ident” course.

A “Home” course is a course that is created, maintained and “owned” by one academic department (aka the “Home” department). The “Home” department is primarily responsible for the decision making and logistical support for the course and instructor.

An “Ident” course is the exact same course as the “Home” (i.e. same instructor, same class time, etc), but is simply being offered to students through another department for purposes of registering under a different department and course number.

Students should, whenever possible, register for their courses under the department number toward which they intend to count the course. For example, an AFAS major should register for the course "Africa: Peoples and Cultures" under its Ident number, L90 306B, whereas an Anthropology major should register for the same course under its Home number, L48 306B.

Grade Options
C=Credit (letter grade)
P=Pass/Fail
A=Audit
U=Satisfactory/Unsatisfactory
S=Special Audit
Q=ME Q (Medical School)

Please note: not all grade options assigned to a course are available to all students, based on prime school and/or division. Please contact the student support services area in your school or program with questions.