Search Constraints
Number of results to display per page
Results for:
Color Theory and Application
Remove constraint Color Theory and Application
Resource Type
MOOC
Remove constraint Resource Type: MOOC
1 - 3 of 3
Search Results
-
MOOC
Design thinking has become very popular recently. It is because many people believe that design thinking can help generate innovative solutions. Many business and non-business organizations are adopting it to resolving their problems. Even business schools and other disciplines include design thinking in their curriculum. Then, what is design thinking, really? And how can it benefit us?
Design thinking is commonly recognized as a problem-solving process that includes five stages - Empathize, Define, Ideate, Prototype and Test. However, when we compare the design thinking process with the conventional problem-solving process, there are no major differences, except the implementation part. Design thinking looks at problems with a holistic and human-centric perspective. It also tackles complex problems by using a non-linear approach. However, some people claim that considering design thinking as a problem-solving process is too simplistic.
Actually, design thinking should be considered as behaviors and attitudes when dealing with problems. Design thinkers use different thinking styles and attitudes when approaching problems. Design thinkers possess certain personal traits like human-centeredness, having a flexible and creative thinking style, being comfortable with subjective and intuitive judgement, and high self-efficacy. These thinking styles and attitudes help not only in problem-solving but also in finding opportunities. In order to be proficient in design thinking, we should not only understand the design Thinking process, but also have to make ourselves become a design thinker.
This MOOC provides you with core knowledge about design thinking and demystifies design thinking as a process for solving complex and wicked problems.
- Keywords:
- Creative ability Product design Critical thinking
- Resource Type:
- MOOC
-
MOOC
This course covers the fundamentals of advanced fluid mechanics: including its connections to continuum mechanics more broadly, hydrostatics, buoyancy and rigid body accelerations, inviscid flow, and the application of Bernoulli’s theorems, as well as applications of control volume analysis for more complex fluid flow problems of engineering interest. This course features lecture and demo videos, lecture concept checks, practice problems, and extensive problem sets.
This course is the first of a three-course sequence in incompressible fluid mechanics: Advanced Fluid Mechanics: Fundamentals, Advanced Fluid Mechanics: The Navier-Stokes Equations for Viscous Flows, and Advanced Fluid Mechanics: Potential Flows, Lift, Circulation & Boundary Layers. The series is based on material in MIT’s class 2.25 Advanced Fluid Mechanics, one of the most popular first-year graduate classes in MIT’s Mechanical Engineering Department. This series is designed to help people gain the ability to apply the governing equations, the principles of dimensional analysis and scaling theory to develop physically-based, approximate models of complex fluid physics phenomena. People who complete these three consecutive courses will be able to apply their knowledge to analyze and break down complex problems they may encounter in industrial and academic research settings.
`The material is of relevance to engineers and scientists across a wide range of mechanical chemical and process industries who must understand, analyze and optimize flow processes and fluids handling problems. Applications are drawn from hydraulics, aero & hydrodynamics as well as the chemical process industries.
- Subjects:
- Mechanical Engineering
- Keywords:
- Fluid mechanics
- Resource Type:
- MOOC
-
MOOC
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.
- Course related:
- COMP4434 Big Data Analytics and EIE6207 Theoretical Fundamental and Engineering Approaches for Intelligent Signal and. Information Processing
- Subjects:
- Computing, Data Science and Artificial Intelligence
- Keywords:
- Artificial intelligence Machine learning
- Resource Type:
- MOOC