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Educational Resources

The Master of Science in Clinical Informatics Management (MCiM)

Dates: Application deadline is November 1, 2021

Cost: $125 application fee (may be waived - read through the learn more section below to find out if eligible)

The Master of Science in Clinical Informatics Management (MCiM) is a unique degree program combining medicine, business and technology. Uniquely blending Stanford’s renowned expertise across medicine, business, and technology, MCiM prepares the next generation of leaders who can efficiently oversee and implement novel uses of technology within health care.


Machine Learning - Stanford/Coursera

Dates: Begins March 15
Cost: Free

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI.

This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

AI for Everyone

Dates: Begins March 17
Cost: Free

AI is not only for engineers. If you want your organization to become better at using AI, this is the course to tell everyone--especially your non-technical colleagues--to take.

In this course, you will learn:

  • The meaning behind common AI terminology, including neural networks, machine learning, deep learning, and data science
  • What AI realistically can--and cannot--do
  • How to spot opportunities to apply AI to problems in your own organization
  • What it feels like to build machine learning and data science projects
  • How to work with an AI team and build an AI strategy in your company
  • How to navigate ethical and societal discussions surrounding AI

Though this course is largely non-technical, engineers can also take this course to learn the business aspects of AI.

Deep Learning Specialization

Dates: Begins March 17
Cost: Free

The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology.

In this Specialization, you will build neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. You will master these theoretical concepts and their industry applications using Python and TensorFlow. You will tackle real-world case studies such as autonomous driving, sign language reading, music generation, computer vision, speech recognition, and natural language processing.

AI is transforming many industries. The Deep Learning Specialization provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI. Along the way, you will get career advice from deep learning experts from industry and academia.

By the end of the program, you'll be able to:

  • Build and train deep neural networks, implement vectorized neural networks, identify key parameters in architecture, and apply deep learning to your applications
  • Use the best practices to train and develop test sets and analyze bias/variance for building DL applications, use standard neural network techniques, apply optimization algorithms, and implement a neural network in TensorFlow
  • Diagnose and use strategies for reducing errors in ML systems, understand complex ML settings, and apply end-to-end learning, transfer learning, and multi-task learning
  • Build a CNN, apply it to visual detection and recognition tasks, use neural style transfer to generate art, and apply these algorithms to image, video, and other 2D/3D data
  • Build and train RNNs, GRUs, and LSTMs, apply RNNs to character-level language modeling, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformers to perform NER and Question Answering

Though this course is largely non-technical, engineers can also take this course to learn the business aspects of AI.

AI in Healthcare Specialization

Dates: Begins March 22
Cost: Free

Artificial intelligence (AI) has transformed industries around the world, and has the potential to radically alter the field of healthcare. Imagine being able to analyze data on patient visits to the clinic, medications prescribed, lab tests, and procedures performed, as well as data outside the health system -- such as social media, purchases made using credit cards, census records, Internet search activity logs that contain valuable health information, and you’ll get a sense of how AI could transform patient care and diagnoses.

In this specialization, we'll discuss the current and future applications of AI in healthcare with the goal of learning to bring AI technologies into the clinic safely and ethically.

This specialization is designed for both healthcare providers and computer science professionals, offering insights to facilitate collaboration between the disciplines.

Data Science and Using Image Processing for Healthcare Professionals

Dates: Thu Aug 27, 9:00 am to 4:00 pm & Fri Aug 28, 9:00 am to 4:00 pm
Cost: $800
Data science and digital image processing are becoming an increasingly integral part of health care. This course exposes you to ways data science is used to extract innovative and actionable insights from healthcare-related datasets and medical imaging.
In this course, we will examine how predictive modeling is used to assess outcomes, needs, and potential interventions. We will also explore medical image analysis which has become an inherent part of medical technology. Participants will have the opportunity to:
  • Install Anaconda on a personal computer.
  • Prepare and explore healthcare-related datasets using the primary tools for data science in Python (e.g., NumPy, Pandas, Matplotlib, Scikit-learn).
  • Examine many of the unique qualities and challenges of healthcare data.
  • Understand how data science is impacting medical diagnosis, prognosis, and treatment.
  • Use a data-science approach to evaluate and learn from healthcare data  (e.g., behavioral, genomic, pharmacological).
  • Use deep learning and TensorFlow to interpret and classify medical images.
  • Perform feature extraction, segmentation, and quantitative measurements of medical images.
  • Understand the increasing importance of data science and image processing in healthcare.