BRWN-ALC-0001 - Artificial Intelligence Applications for Health Data
Designed for professionals keen to harness the power of contemporary AI technologies
Artificial Intelligence is fundamentally changing how the world operates. There is probably no sector or discipline, from agriculture and finance to humanities and autonomous driving, that has not used AI. Professionals with AI skills are highly sought-after; the US Bureau of Labor Statistics predicts the demand to increase by 28% or 11.5 million new jobs through 2026, outpacing all other industries. One of AI’s undisputable advantages over most other technologies is its high transferability, significantly lowering industry-specific barriers to its applications.
The Advanced Learning Certificate in Artificial Intelligence Applications for Health Data is designed for professionals interested in contemporary AI technologies and seeking to build skills with AI’s applications to health or other “big data” for their work or research. This program is designed to take you from an AI novice to a confident practitioner with integrated case studies and programming demonstrations using Google Colab. It will provide hands-on, step-by-step guidance, and ample real-world practice opportunities for professionals to learn and apply AI tools and models to solve real-world health and social problems.
ONLINE PROGRAM: This 15-week program includes online class meetings on Thursday mornings via Zoom, 7:00 – 10:00 a.m. (US Central Time).
September 5 – December 19, 2024 (no class November 28)
Continuing Education Information:
- 45 Missouri/Illinois Social Work CEUs
- 45 public health CPH units
AN APPROVED APPLICATION IS REQUIRED BEFORE ENROLLING IN THIS COURSE. APPLY TODAY!
Application Deadline: August 21st by 5:00 p.m., Central Time.
Course Outline
This program is intended for individuals who have a demonstrated interest in increasing their skills in applying the power of AI techniques to health data for research or industry purposes. 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 three parts, building from basic tools to more advanced applications:
- Weeks 1-2 – Participants will receive an overview of artificial intelligence, unveil the mystery of AI and machine learning, learn to code in Python, use NumPy and Pandas to master data wrangling, and use Matplotlib for data visualization.
- Weeks 3-7 – Class content will focus on machine learning applications. We will learn topics including classification and regression, model training and validation, support vector machines, decision trees, ensemble methods like Random Forest and XGBoost, dimensionality reduction, unsupervised learning, and auto ML.
- Weeks 8-15 – The final portion of the program considers deep learning applications (i.e., neural networks). Topics covered include a foundation with neural networks; computer vision for image classification, object detection, image segmentation, and image generation; natural language processing for text classification (sentiment analysis), language generation, text translation, chatbot, and prompt engineering; introduction to recommender systems; introduction to time series forecasting; and 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.
Learner Outcomes
By the end of the program, participants will:
- Gain a deep understanding of the key concepts and elements of AI, machine learning, and deep learning (neural networks)
- Be familiar with a comprehensive pool of popular, state-of-the-art AI models and their applications in public health and beyond
- Understand the strengths, limitations, and tradeoffs of different AI models and best practices in implementing them
- Understand sources of data biases, principles of data ethics, and how to avoid or reduce biases to build ethical, responsible AI models
- Be proficient in using Python in conjunction with popular APIs and cloud platforms to implement state-of-the-art AI models on various data types
- Be able to apply AI models to better understand and address health data-related concerns or other social problems
Successful completion of the certificate program includes attendance and participation in weekly class meetings, as well as completion of weekly-oriented application assignments. CLICK HERE for more in-depth course details.
Prerequisites
- An approved application is required before enrolling in this course.
- 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 browncertificates@wustl.edu or through WeChat (ID: WUSTLBrownSchool).