Stanford University

This course should be taken after the Essentials of Palliative Care course and continues building your primary palliative care skills – communication, psychosocial support and goals of care. You will learn how to screen, assess, and manage both physical and psychological symptoms. You will explore common symptoms such as [...]

With artificial intelligence applications proliferating throughout the healthcare system, stakeholders are faced with both opportunities and challenges of these evolving technologies. This course explores the principles of AI deployment in healthcare and the framework used to evaluate downstream effects of AI healthcare [...]

Machine learning and artificial intelligence hold the potential to transform healthcare and open up a world of incredible promise. But we will never realize the potential of these technologies unless all stakeholders have basic competencies in both healthcare and machine learning concepts and principles. This course will introduce the [...]

Solving the problems and challenges within the U.S. healthcare system requires a deep understanding of how the system works. Successful solutions and strategies must take into account the realities of the current system. This course explores the fundamentals of the U.S. healthcare system. It will introduce the principal institutions [...]

This course offers an intimate, story-based introduction to the experiences of six transgender children and their families. Through illustrated stories and short teaching videos, learners will gain a better understanding of gender identity and the gender spectrum. Stanford physicians, K-12 educators, and transgender faculty members offer practical tips for [...]

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine [...]

The primary topics in this part of the specialization are: greedy algorithms (scheduling, minimum spanning trees, clustering, Huffman codes) and dynamic programming (knapsack, sequence alignment, optimal search trees).

The primary topics in this part of the specialization are: data structures (heaps, balanced search trees, hash tables, bloom filters), graph primitives (applications of breadth-first and depth-first search, connectivity, shortest paths), and their applications (ranging from deduplication to social network [...]

The primary topics in this part of the specialization are: asymptotic ("Big-oh") notation, sorting and searching, divide and conquer (master method, integer and matrix multiplication, closest pair), and randomized algorithms (QuickSort, contraction algorithm for min cuts).

In this introductory, self-paced course, you will learn multiple theories of organizational behavior and apply them to actual cases of organizational change. Organizations are groups whose members coordinate their behaviors in order to accomplish a shared goal. They can be found nearly everywhere in today’s society: universities, start-ups, classrooms, [...]