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Advancing Social Work, Public Health & Social Policy Menu Academics Master of Social Work Master of Public Health Master of Social Policy Dual Degrees 3-2 Programs PhD in Social Work PhD in Public Health Sciences AIBDA Certificate Global Opportunities Field Education Faculty &＃038; Research Faculty Professors of Practice Research Professors Teaching Professors, Senior Lecturers &＃038; Scholars Visiting &＃038; Adjunct Emeritus Faculty Research Centers Faculty Recruitment Resources &＃038; Initiatives Professional Development Clark-Fox Policy Institute Community Partnerships Field Instructor Resources Driving Equity 2030 Open Classroom Advanced Learning Certificates Grand Challenges for Social Work News Most Recent News Alumni Students Social Work Practicum Public Health Policy Faculty Research Community Engagement Diversity Global Life at Brown Admitted Student Resources Life at Brown School Our Facilities Student Support Student Groups & Events Student Body Profile Equity, Diversity and Inclusion Career Engagement International Student Support Brown School Library Student Blog St. Louis Region Washington University Apply About Driving Equity 2030 Events Alumni Contact Us Brown School › Resources & Initiatives › Professional Development › Advanced Learning Certificate › ​Artificial Intelligence Applications for Health Data Artificial Intelligence Applications for Health DataAn Advanced Learning Certificate for data science beginners who want to master the power of contemporary AI technology Artificial intelligence is changing the way the world works, and those with AI training are increasingly in demand. This 15-week course is designed for non-STEM health and social work professionals who are eager to explore the possibilities of AI but have limited or no experience with statistics and programming. Through step-by-step guidance, integrated case studies, and hands-on practice, this course will introduce key concepts behind contemporary AI technologies and provide practical instruction on how to apply these technologies to real-world health and social issues. By the end of the program, you will be able to confidently use AI tools, including data models, machine learning, and neural networks, to leverage data find solutions in your day-to-day work. Learn more about your instructor, Dr. Ruopeng An, and review program details and prerequisites below. Applications for this program will close on August 21, 2024, at 5 p.m. CDT. Apply Now! Online Program. This 15-week program includes online class meetings on Thursday mornings by Zoom, 7:00 &＃8211; 10:00 a.m. (Central Time). September 5 &＃8211; December 19, 2024 (no class November 28) Pricing: General Admission: $1995 10% discount for WashU Faculty/Staff/Student 10% early registration discount is available to all applicants until June, 30th 2024 10% discount is available to all ICNYSN and CHPMS referral applicants Continuing Education Information: 45 Missouri/Illinois social work CEUs 45 public health CPH units Instructor: Ruopeng An, PhD, MPP, FACE, FAAHB Associate Professor, Brown School Questions? Please contact us at 314.935.7573 or [email protected] Want to know more? This program is designed for non-CS and non-STEM professionals eager to master the power of contemporary AI technologies. You do not have to be an alum of the Brown School to apply for consideration. This is an online program, with scheduled class meetings on Thursday mornings conducted by Zoom and self-paced content, such as readings and assignments. Applicants should be prepared to commit 5 hours of total effort per week. Reading assignments come from provided texts, which are included in the program fee. The program presupposes a basic knowledge of statistics and previous experience working with a statistical software package. Students should have passed an introductory statistics class and have experience with R, SAS, SPSS, or Stata, or otherwise using basic quantitative skills within the last five years. Experience with Python is helpful but not required. The certificate program is divided into five parts, building from basic tools to more advanced applications: Weeks 1-2: Participants will receive an overview of artificial intelligence and machine learning. They will learn to code in Python, master data wrangling with NumPy and Pandas, and use Matplotlib for data visualization. Weeks 3-7: The focus will shift to machine learning applications. Topics include: Classification and regression Model training and validation Support vector machines and decision trees Ensemble methods like Random Forest and XGBoost Dimensionality reduction Unsupervised learning techniques Week 8: This session will cover genetic algorithms, inspired by natural selection, as tools for solving optimization problems. Participants will learn the fundamental principles and components of genetic algorithms, such as selection, crossover, and mutation, and implement these methods using the DEAP module, a Python library for evolutionary algorithms. Week 9: The focus will be on reinforcement learning (RL), where agents make decisions in complex environments. This week will cover: Core principles of RL, including agents, environments, rewards, policies, and exploration vs. exploitation Implementation of RL algorithms using Python&＃8217;s Gym and Stable Baselines libraries Weeks 10-15: The final portion of the program focuses on deep learning applications, specifically neural networks. Topics include: Neural network fundamentals Computer vision for image classification, object detection, and image segmentation Natural language processing for text classification (sentiment analysis) using Huggingface transformers Prompt engineering principles and best practices Generative AI for image and text generation using OpenAI API and LangChain Retrieval augmented generation (RAG) and its hands-on applications Creating synthetic data for research and analysis Weekly assignments will help participants master the topics covered in the lectures/labs using real-world datasets related to health and beyond. Successful completion of the certificate program includes attendance and participation in weekly class meetings, as well as completion of weekly-oriented application assignments. Upon completion of this training program, participants will emerge as experts with a thorough, working-level understanding of all four major domains of modern AI: machine learning, deep learning, reinforcement learning, and evolutionary algorithms. They will be proficient in implementing end-to-end solutions using state-of-the-art AI tools to solve real-world problems. ​How to Apply Complete program applications include: Submission of your current resume and applicant information Educational history &＃8211; a transcript is not required Admissions decisions will be made on a rolling basis. Generally it is possible to provide an admissions decision within two weeks of receipt of a completed application. Once your application is reviewed and approved, you will receive an acceptance email with further instructions on how to enroll online. Keep track of your application status in your profile under My Applications. Applications for this program will close on August 21, 2024 at 5:00 p.m., US Central Time. Admission prerequisites: Applicants should have (or be able to obtain) access to a computer, along with a webcam and reliable internet service. Introductory knowledge of statistics, including working with a statistical software package such as R, SAS, SPSS, Stata, or equivalent.​​ All students must be willing to comply with Washington University policies. Please note that pursuing the AI Certificate course while on OPT (Optional Practical Training) is not permitted. For questions, email us at [email protected] or through WeChat (ID: WUSTLBrownSchool). More Info ​ Meet Your Instructor Ruopeng An, PhD, MPP, FACE, FAAHBAssociate Professor, Brown School Dr. An is a self-taught AI expert and interdisciplinary data scientist. He serves as Faculty Lead in Public Health Sciences at the Brown School and the Division of Computational & Data Sciences at Washington University in St. Louis. He serves as Faculty Fellow for AI Innovations in Education at the Provost’s Office, directs the AI and Data Science Core at the NIH Center for Diabetes Translation Research, and leads digital transformation strategic planning at the Brown School. His AI talks have attracted tens of thousands, and hundreds of students from various disciplines have taken his courses or graduated from his AI programs. With over 210 peer-reviewed journal publications, he is recognized as one of Elsevier’s top 2% most cited scientists. His work has been highlighted by media outlets including Time, the New York Times, the Los Angeles Times, the Washington Post, Reuters, USA Today, Bloomberg, Forbes, the Atlantic, the Guardian, Fox News, NPR, and CNN. He serves on research grants and expert panels for NIH, CDC, NSF, HHS, USDA, and the French National Research Agency. He is an elected Fellow of the American College of Epidemiology and the American Academy of Health Behavior. His research has been funded by federal agencies and public/private organizations (e.g., OpenAI, Abbott, Amgen). He has wide teaching and methodological expertise, including applied artificial intelligence (machine learning, deep neural networks, generative AI), quantitative policy analysis (causal inference, cost-benefit and cost-effectiveness analysis, and microsimulation), applied econometrics and regression analysis, and systematic review and meta-analysis. He founded and chairs two AI and data science certificate programs at WashU and hosts the “Artificial Intelligence in the Social Sciences” Open Classroom series. Dr. An on this Advanced Learning Certificate: “The Curse of Knowledge has eroded the way we teach AI and data science. Layers of math, statistics, programming principles, and jargon have buried the essence of AI, turning it into a cold, intimidating corpse under the pyramid. My solution is to invert the pyramid, focusing on hands-on, real-world AI applications, bringing coding to life, and referencing math and statistics only when absolutely necessary for problem-solving.” See testimonials from previous certificate program participants. Join Professor Ruopeng An for an introductory glimpse into the Artificial Intelligence Applications for Health Data Certificate program. “&＃8221;I am excited to have a new toolbox with AI to think about my patients and their care. Thank you for this training and guidance.&＃8221;&＃8221;–David Molter, MD​ Professor of Pediatric Otolaryngology AI Certificate Alum 22&＃8242; Contact UsExternal Affairs Gary Parker, PhD Associate Dean of External Affairs Brown School at Washington University in St. Louis MSC 1196-0251-461 Brookings Drive St. Louis, MO 63105-4899 (314) 935-7573 | [email protected] ABOUT US | DIRECTIONS | FAQ&＃8217;s | JOIN EMAIL LIST | POLICIES Quick Links: Apply About Brown School Contact Us Directions Academic Professional Integrity Policy Native Land Acknowledgement wustl.edu Keep Up with Our Latest News, Research and Insights: Support the Campaign: to Grow. to Lead. to Change. --> Founded in 1925, the George Warren Brown School was named with a generous gift from Betty Bofinger Brown in memory of her late husband. Copyright 2024 by Brown School at Washington University in St. Louis Washington University  •  One Brookings Drive   •  St. Louis, MO 63130