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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:
- EIE6207 Theoretical Fundamental and Engineering Approaches for Intelligent Signal and. Information Processing and COMP4434 Big Data Analytics
- Subjects:
- Computing
- Keywords:
- Artificial intelligence Machine learning
- Resource Type:
- MOOC
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MOOC
This is CS50x , Harvard University's introduction to the intellectual enterprises of computer science and the art of programming for majors and non-majors alike, with or without prior programming experience. An entry-level course taught by David J. Malan, CS50x teaches students how to think algorithmically and solve problems efficiently. Topics include abstraction, algorithms, data structures, encapsulation, resource management, security, software engineering, and web development. Languages include C, Python, SQL, and JavaScript plus CSS and HTML. Problem sets inspired by real-world domains of biology, cryptography, finance, forensics, and gaming. The on-campus version of CS50x , CS50, is Harvard's largest course.
- Course related:
- COMP1011 Programming Fundamentals
- Subjects:
- Computing
- Keywords:
- Computer programming Computer science
- Resource Type:
- MOOC
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MOOC
This course covers a wide variety of topics in machine learning and statistical modeling. While mathematical methods and theoretical aspects will be covered, the primary goal is to provide students with the tools and principles needed to solve the data science problems found in practice. This course also serves as a foundation on which more specialized courses and further independent study can build. This course was designed as part of the core curriculum for the Center for Data Science's Masters degree in Data Science. Other interested students who satisfy the prerequisites are welcome to take the class as well. Note that class is intended as a continuation of DS-GA-1001 Intro to Data Science, which covers some important, fundamental data science topics that may not be explicitly covered in this DS-GA class (e.g. data cleaning, cross-validation, and sampling bias).
- Course related:
- LGT6801 Guided Study in Logistics I
- Subjects:
- Computing and Mathematics and Statistics
- Keywords:
- Big data Data mining Machine learning Mathematical statistics -- Data processing
- Resource Type:
- MOOC
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MOOC
Video games as a medium go back more than 50 years to mainframe computers. Even the central design of video games can be traced back to the first games themselves. To be a good game designer, it's essential to have an understanding of the video game design industry's fascinating history. We've partnered with The Strong National Museum of Play to give you a unique look into the history of all things video game. The International Center for the History of Electronic Games at The Strong is the largest and most comprehensive public assemblage of video games and related materials in the world. The staff are celebrated experts in the field and the ICHEG is visited by scholars of video games from around the world. You'll gain amazing insight into the history of video games with a guided exploration of key artifacts from the collection of more than 100,000 electronic games and materials.
- Subjects:
- Interactive and Digital Media and Computing
- Keywords:
- Video games -- Design History
- 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
Modern video games are incredibly complex multimedia productions involving still and motion graphics, code, audio, interface elements, narrative elements and much more. In this course, you will learn how and where all these pieces come from, who's in charge of each piece and the different stages of the game design process. We will also show you how everything is brought together to create a final product.
- Subjects:
- Interactive and Digital Media and Computing
- Keywords:
- Video games
- Resource Type:
- MOOC
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MOOC
Virtual reality is changing the way we interact with the world. But how does it work, what hardware is involved, and how is software written for it? In this course, part of the Virtual Reality Professional Certificate program, we will explore the foundations of user-friendly virtual reality app development for consumers, as well as enterprise solutions. Both hardware and software aspects will be discussed. You will learn to evaluate devices necessary for virtual reality applications, what their differences are, how you write interactive applications for virtual reality, and we will discuss the most frequent problems you are going to need to solve to write virtual reality software. In this course, you will explore the basics of virtual reality software through copying and modifying JavaScript to explore tradeoffs in VR application design. Extensive programming experience is not required. By the end of this course, you will understand what is important for successful virtual reality software and learn how to write simple virtual reality programs themselves with WebVR. This course is taught by an instructor with almost two decades of experience in virtual reality who leads the Immersive Visualization Laboratory at UC San Diego.
- Subjects:
- Interactive and Digital Media and Computing
- Keywords:
- Computer simulation Virtual reality Human-computer interaction
- Resource Type:
- MOOC
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MOOC
Many natural and man-made structures can be modeled as assemblages of interconnected structural elements loaded along their axis (bars), in torsion (shafts) and in bending (beams). In this course you will learn to use equations for static equilibrium, geometric compatibility and constitutive material response to analyze structural assemblages. This course provides an introduction to behavior in which the shape of the structure is permanently changed by loading the material beyond its elastic limit (plasticity), and behavior in which the structural response changes over time (viscoelasticity). This is the second course in a 3-part series. In this series you will learn how mechanical engineers can use analytical methods and “back of the envelope” calculations to predict structural behavior. The three courses in the series are: Part 1 – 2.01x: Elements of Structures. (Elastic response of Structural Elements: Bars, Shafts, Beams). Fall Term Part 2 – 2.02.1x Mechanics of Deformable Structures: Part 1. (Assemblages of Elastic, Elastic-Plastic, and Viscoelastic Bars in axial loading). Spring Term Part 3 – 2.02.2x Mechanics of Deformable Structures: Part 2. (Assemblages of bars, shafts, and beams. Multi-axial Loading and Deformation. Energy Methods). Summer Term
- Subjects:
- Mechanical Engineering
- Keywords:
- Strength of materials Deformations (Mechanics)
- 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
Electric powertrains are estimated to propel a large part of road vehicles in the future, due to their high efficiency and zero tailpipe emissions. But, the cost and weight of batteries and the time to charge them are arguments for the conventional powertrain in many vehicles. This makes it important for engineers working with vehicles to understand how both these powertrains work, and how to determine their performance and energy consumption for different type of vehicles and different ways of driving vehicles. This course is aimed at learners with a bachelor's degree or engineers in the automotive industry who need to develop their knowledge about electric powertrains. In this course, you will learn how electric and conventional combustion engine powertrains are built and how they work. You will learn methods to calculate their performance and energy consumption and how to simulate them in different driving cycles. You will also learn about the basic function, the main limits and the losses of: Combustion engines, Transmissions Electric machines, Power electronics Batteries. This knowledge will also be a base for understanding and analysing different types of hybrid vehicles, discussed in the course, Hybrid Vehicles. As a result of support from MathWorks, students will be granted access to MATLAB/Simulink for the duration of the course.
- Subjects:
- Electrical Engineering, Mechanical Engineering, and Transportation
- Keywords:
- Electric vehicles Automobiles -- Power trains
- Resource Type:
- MOOC
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