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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
- Keywords:
- Machine learning Artificial intelligence
- Resource Type:
- MOOC
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MOOC
Game designers work with a wide range of asset creators, programmers, producers, and others to bring a video game from concept to product. In this course, you will learn about the different types of teams a game designer is a member of, both large and small.
- Subjects:
- Interactive and Digital Media and Computing
- Keywords:
- Video games -- Design
- Resource Type:
- MOOC
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MOOC
In this course you will learn about a wide variety of Web 2.0 tools to use in your teaching and learning. Web 2.0 tools provide innovative ways to communicate, present content, and collaborate with others in creative ways. Web 2.0 tools are easy to learn, use, and implement, and many are free. This course will not only introduce you to popular Web 2.0 tools like Edmodo, Twitter, Voicethread, and Skype in K-16 instruction, but you will also learn how to effectively integrate these technologies into your classroom practices and create engaging student activities.
- Keywords:
- Internet in education Computer-assisted instruction Educational technology Educational innovations
- Resource Type:
- MOOC
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MOOC
In autonomous vehicles such as self-driving cars, we find a number of interesting and challenging decision-making problems. Starting from the autonomous driving of a single vehicle, to the coordination among multiple vehicles. This course will teach you the fundamental mathematical model for many of these real-world problems. Key topics include Markov decision process, reinforcement learning and event-based methods as well as the modelling and solving of decision-making for autonomous systems. This course is aimed at learners with a bachelor's degree or engineers in the automotive industry who need to develop their knowledge in decision-making models for autonomous systems. Enhance your decision-making skills in automotive engineering by learning from Chalmers, one of the top engineering schools that distinguished through its close collaboration with industry.
- Subjects:
- Electrical Engineering, Mechanical Engineering, and Transportation
- Keywords:
- Decision making Automobiles -- Design construction Automated vehicles
- Resource Type:
- MOOC
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MOOC
How can we strengthen sustainability? By empowering individuals and communities to transform and balance dynamic natural resources, economic prosperity, and healthy populations. In this course, you’ll explore productive and disruptive social, ecological, and economic intersections – the “triple bottom line.” You’ll investigate a spectrum of global, national, regional, municipal and personal relationships that are increasing resiliency. Most importantly, you’ll learn how to effectively locate your interests, and to leverage optimistic change within emerging 21st century urban environments. This course will describe fundamental paradigm shifts that are shaping sustainability. These include connectivity, diversity, citizen engagement, collaboration source tracing, mapping, transportation, and integrative, regenerative design. We will take examples from cities around the globe; making particular use of the complex evolution of site-specific conditions within the Connecticut River watershed. In addition we will present tools and strategies that can be utilized by individuals, communities, and corporations to orchestrate effective and collective change. Each week, lessons will highlight the significance of clean water as a key indication of ecosystem, community and human health. Learners will be asked to investigate and share information about their local environment. Finally, we will note the impact of such disruptive forces as industrial pollution, changing governance, privatization of public services, mining of natural resources, public awareness, and climate change. A fundamental course goal will be to characterize indicators of economic prosperity and happiness that relate to environmental sustainability – and the capacity of individuals to create change.
- Subjects:
- Environmental Engineering, Building Services Engineering, and Building and Real Estate
- Keywords:
- Urban ecology (Sociology) Sustainable development
- 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