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Collaboration and Mentorship in Research
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MOOC
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MOOC
The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications.
This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field.
This 3-course Specialization is an updated version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012.
It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.) By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.
- Course related:
- AAE5103 Artificial Intelligence in Aviation Industry
- Subjects:
- Computing, Data Science and Artificial Intelligence
- Keywords:
- Artificial intelligence Machine learning
- Resource Type:
- MOOC
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MOOC
This course is about resonating with your experience and meaningfully engaging patients to make good decisions and to address the significance of interprofessional collaborations in health care. Service users’ experience and views across all points on health promotion, management and support services are crucial to developing optimal health care practice. Join Prof Elwynis a leader committed in Shared Decision Making (SDM) practice and research to promote high quality decision making. Taking into account the best scientific evidence available, he will explain to you how this collaborative process and the use of decision-aids help eliciting patients’ beliefs and integrating patient preferences and priorities to treatment options after thorough considerations of the trade-offs. Together, we are oriented to the interprofessional collaborative initiative that synergizes the strengths among health care allies toachieve optimal clinical practice and health outcomes. Renowned experts in various health care fields share their first hand experiences, eliciting profound insights and wisdoms about interprofessional collaborations. This is aspirational in learning to reflect, decipher, interpret and construct ways in enhancing effective coordination of care to meet health needs. Making sense of the SDM and IPC concepts and recognizing the available evidences and resources is crucial to enabling good team dynamics. Using a docu-drama, it takes you through a patient’s journey having a stroke due to his hidden assumptions in receiving treatment to atrial fibrillation (an abnormal heart rhythm). His attitude and struggles point to a challenging recovery process. Contemplate on how SDM and IPC could step in at different stages to improve health outcomes. Identifying gaps in the existing scientific evidence and services will help you to pursue influential strategies and design innovative programs or products to attain better outcomes. Your understanding and participation in this course will create positive impact over time in advancing the present health system to deliver the best possible outcomes to various stakeholders. We are excited to see your passion in affecting health decisions and determination in accomplishing excellent care delivery. Get connected with a global community of learners and simply enjoy gaining new ideas about making a difference in health care.
- Subjects:
- Health Sciences
- Keywords:
- Patient participation Clinical medicine -- Decision making Medical care -- Decision making
- Resource Type:
- MOOC
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MOOC
Autonomous vehicles, such as self-driving cars, rely critically on an accurate perception of their environment. In this course, we will teach you the fundamentals of multi-object tracking for automotive systems. Key components include the description and understanding of common sensors and motion models, principles underlying filters that can handle varying number of objects, and a selection of the main multi-object tracking (MOT) filters. The course builds and expands on concepts and ideas introduced in CHM013x: ""Sensor fusion and nonlinear filtering for automotive systems"". In particular, we study how to localize an unknown number of objects, which implies various interesting challenges. We focus on cameras, laser scanners and radar sensors, which are all commonly used in vehicles, and emphasize on situations where we seek to track nearby pedestrians and vehicles. Still, most of the involved methods are more general and can be used for surveillance or to track, e.g., biological cells, sports athletes or space debris. The course contains a series of videos, quizzes and hands-on assignments where you get to implement several of the most important algorithms. Learn from award-winning and passionate teachers to enhanceyour knowledge at the forefront of research on self-driving vehicles. Chalmers is among the top engineering schools that distinguish itself through its close collaboration with industry.
- Subjects:
- Electrical Engineering, Mechanical Engineering, and Transportation
- Keywords:
- Automobiles -- Design construction Computer vision Automated vehicles
- Resource Type:
- MOOC