Stanford University

 

 


 

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E.g., 2017-08-16
E.g., 2017-08-16
E.g., 2017-08-16
Aug 21st 2017

The primary topics in this part of the specialization are: shortest paths (Bellman-Ford, Floyd-Warshall, Johnson), NP-completeness and what it means for the algorithm designer, and strategies for coping with computationally intractable problems (analysis of heuristics, local search).

Average: 5 (2 votes)
Aug 21st 2017

Learn how to think the way mathematicians do - a powerful cognitive process developed over thousands of years. Mathematical thinking is not the same as doing mathematics – at least not as mathematics is typically presented in our school system. School math typically focuses on learning procedures to solve highly stereotyped problems. Professional mathematicians think a certain way to solve real problems, problems that can arise from the everyday world, or from science, or from within mathematics itself.

Average: 7.2 (6 votes)
Aug 21st 2017

In this course we will seek to “understand Einstein,” especially focusing on the special theory of relativity that Albert Einstein, as a twenty-six year old patent clerk, introduced in his “miracle year” of 1905. Our goal will be to go behind the myth-making and beyond the popularized presentations of relativity in order to gain a deeper understanding of both Einstein the person and the concepts, predictions, and strange paradoxes of his theory.

Average: 7.7 (7 votes)

Aug 21st 2017

Learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. 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.

Average: 7.5 (32 votes)
Aug 14th 2017

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).

Average: 7.4 (5 votes)
Aug 14th 2017

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 analysis).

Average: 6.8 (4 votes)

Aug 14th 2017

Popularized by movies such as "A Beautiful Mind," game theory is the mathematical modeling of strategic interaction among rational (and irrational) agents. Beyond what we call `games' in common language, such as chess, poker, soccer, etc., it includes the modeling of conflict among nations, political campaigns, competition among firms, and trading behavior in markets such as the NYSE.

Average: 4.8 (4 votes)
Aug 14th 2017

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).

Average: 6.2 (6 votes)
Aug 14th 2017

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, hospitals, non-profits, government bureaus, corporations, restaurants, grocery stores, and professional associations are some of many examples of organizations.

Average: 6.9 (15 votes)
Aug 14th 2017

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 learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems.

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Aug 14th 2017

This course offers an intimate, story-based introduction to the real-life 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.

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Aug 14th 2017

This course will explore the concept of agape love (compassion/kindness) as a force for social justice and action and as the inspiration for service and the application of knowledge to positive social change. Biological, psychological, religious, and social perspectives of love will be discussed, drawing on the expertise of people from a variety of disciplines.

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