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MOOC
Those who work in modern language service industry are required to be capable of using computers and Internet to aid their translation job by adapting a variety of efficient tools, rather than just using word processor tools and several basic computer-aided translation software. This course teaches the basic concepts of computer-aided translation technology, helps students learn to use a variety of computer-aided translation tools, enhances their ability to engage in various kinds of language service in such a technical environment, and helps them understand what the modern language service industry looks like. This course covers introduction to modern language services industry, basic principles and concepts of translation technology, information technology used in the process of language translation, how to use electronic dictionaries, Internet resources and corpus tools, practice of different computer-aided translation tools, translation quality assessment, basic concepts of machine translation, globalization, localization and so on. As a compulsory course for students majoring in Translation and Interpreting, this course is also suitable for students with or without language major background. By learning this course, students can better understand modern language service industry and their work efficiency will be improved for them to better deliver translation service. The course is one of the PKU-DeTao MOOCs, which is a joint effort by Peking University and DeTao Masters Academy.
- Subjects:
- Translating and Interpreting and Computing
- Keywords:
- Machine translating Translating interpreting
- Resource Type:
- MOOC
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MOOC
本系列課程從零開始，教授一般認為最適合初學者的程式語言「Python」，目標是讓大家在完成本課程之後，一方面獲得程式設計與運算思維的基本概念，一方面也能獨立寫出能解決運算問題的程式。本課程和一般程式設計課程最不同的地方，在於它是以解決商管領域的運算問題為導向，因此課程不會只含有質因數分解、紅球白球排列組合、三角不等式、萬年曆、數字排序等傳統程式設計課程的範例與作業，而是包含了生產、物流、存貨、投資、定價等問題，讓大家在學會程式設計的同時，也直接體會程式設計與資訊技術在商管領域的各種應用。 本系列課程共分為三門課程。本門課程做為第一門課程，將介紹程式設計的基本觀念、Python 語言的基本語法、選擇、迴圈、清單，並以作業管理領域的一些簡單演算法作結。
- Course related:
- MM2425 Introduction to Business Analytics
- Subjects:
- Computing
- Keywords:
- Python (Computer program language)
- Resource Type:
- MOOC
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MOOC
After completed this online course, you will be able to: (1) read the blockchain-related news easily; (2) explain 99% of blockchain vocabulary in one minute and (3) to publish the exclusive cryptocurrency on the blockchain within one click.
- Course related:
- APSS3225 Media and Society
- Subjects:
- Computing
- Keywords:
- Cryptocurrencies Blockchains (Databases)
- 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
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
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
We encounter signals and systems extensively in our day-to-day lives, from making a phone call, listening to a song, editing photos, manipulating audio files, using speech recognition softwares like Siri and Google now, to taking EEGs, ECGs and X-Ray images. Each of these involves gathering, storing, transmitting and processing information from the physical world. This course will equip you to deal with these tasks efficiently by learning the basic mathematical framework of signals and systems. This course is divided into two parts. In this part (EE210.1x), we will explore the various properties of signals and systems, characterization of Linear Shift Invariant Systems, convolution and Fourier Transform, while the next part (EE210.2x), will deal with the Sampling theorem, Z-Transform, discrete Fourier transform and Laplace transform. Ideas introduced in this course will be useful in understanding further electrical engineering courses which deal with control systems, communication systems, power systems, digital signal processing, statistical signal analysis and digital message transmission. The concepts taught in this course are also useful to students of other disciplines like mechanical, chemical, aerospace and other branches of engineering and science.
- Course related:
- EE3008A Linear Systems and Signal Processing
- Subjects:
- Electrical Engineering
- Keywords:
- Signal processing
- Resource Type:
- MOOC
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MOOC
In this course, we will introduce you to the fundamentals of sensor fusion for automotive systems. Key concepts involve Bayesian statistics and how to recursively estimate parameters of interest using a range of different sensors. The course is designed for students who seek to gain a solid understanding of Bayesian statistics and how to use it to fuse information from different sensors. We emphasize object positioning problems, but the studied techniques are applicable much more generally. The course contains a series of videos, quizzes and hand-on assignments where you get to implement many of the key techniques and build your own sensor fusion toolbox. The course is self-contained, but we highly recommend that you also take the course ChM015x: Multi-target Tracking for Automotive Systems. Together, these courses give you an excellent foundation to tackle advanced problems related to perceiving the traffic situation around an autonomous vehicle using observations from a variety of different sensors, such as, radar, lidar and camera.
- Subjects:
- Electrical Engineering, Mechanical Engineering, and Transportation
- Keywords:
- Automobiles -- Electronic equipment Automotive sensors
- Resource Type:
- MOOC
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MOOC
This Specialization builds on the success of the Python for Everybody course and will introduce fundamental programming concepts including data structures, networked application program interfaces, and databases, using the Python programming language. In the Capstone Project, you’ll use the technologies learned throughout the Specialization to design and create your own applications for data retrieval, processing, and visualization.
- Subjects:
- Computing
- Keywords:
- Python (Computer program language)
- 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
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MOOC
Modeling, control design, and simulation are important tools supporting engineers in the development of automotive systems, from the early study of system concepts (when the system possibly does not exist yet) to optimization of system performance. This course provides a theoretical basis to model-based control design with the focus on systematically develop mathematical models from basic physical laws and to use them in control design process with specific focus on automotive applications. You will learn the basics of mathematical modeling applied to automotive systems, and based on the modeling framework different type of controller and state estimation methods will be introduced and applied. Starting from a pure state-feedback concept down to optimal control methods, with special attention on different automotive applications. Different methods for state reconstruction is also introduced and discussed in the course. Exercises play an important rolethroughout the course. This course is aimed at learners with a bachelor's degree or engineers in the automotive industry who need to learn more about mathematical modelling of automotive systems.
- Subjects:
- Electrical Engineering, Mechanical Engineering, and Transportation
- Keywords:
- Automobiles -- Design construction -- Mathematical models Motor vehicles -- Dynamics
- Resource Type:
- MOOC
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MOOC
This course explores the topic of solid objects subjected to stress and strain. The methods taught in the course are used to predict the response of engineering structures to various types of loading, and to analyze the vulnerability of these structures to various failure modes. Axial loading with be the focus in this course.
- Course related:
- ME3303 Mechanics of Solids
- Subjects:
- Mechanical Engineering
- Keywords:
- Strength of materials Mechanics Applied Materials
- 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
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
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
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
Learn about the integrative power of knowledge management, Big Data and Cloud Computing, and how they impact the new business era.
- Subjects:
- Business Information Technology, Management, Computing, and Industrial and Systems Engineering
- Keywords:
- Big data Knowledge management
- Resource Type:
- MOOC
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MOOC
Aviation is essential for global business as it supports worldwide travelling and cargo transportation. Before COVID-19, the economic benefits generated by the industry across countries showed a growing trend. This course is aimed to provide learners with a broad understanding of airport services in different phases in terms of design and engineering. Learners will receive basic knowledge of how an airport operates. Key topics include the future perspective of air traffic control, air traffic flow management, airport management, facility planning, airport terminal design for the post-pandemic world and ground services.
- Subjects:
- Aeronautical and Aviation Engineering and Logistics
- Keywords:
- Airports -- Management Aeronautics Commercial -- Management Airlines -- Management
- Resource Type:
- MOOC
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MOOC
New to web design? Start here first. Instructor James Williamson introduces the fundamental concepts, tools, and learning paths for web design. He explains what it means to be a web designer, the various areas of specialization, and whether web design is the right hobby or career for you. Along the way, he talks to five prominent designers and developers, who have each found success in a different corner of the web. If you want to get up and running fast, check out the chapter on getting online, choosing a domain name and web host, and getting around the backend of a standard website. Need to stock your tool chest? Learn what you'll need to build a brand new site, from web design software and content management systems, to testing and prototyping tools and development frameworks. Finally, James outlines learning paths for where to go next, touching on subjects such as standards and accessibility, responsive design, and the three core web technologies: HTML, CSS, and JavaScript.
- Subjects:
- Computing
- Keywords:
- Web sites -- Design
- Resource Type:
- MOOC
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MOOC
In this course, you'll learn the fundamentals of the Python programming language, along with programming best practices. You’ll learn to represent and store data using Python data types and variables, and use conditionals and loops to control the flow of your programs. You’ll harness the power of complex data structures like lists, sets, dictionaries, and tuples to store collections of related data. You’ll define and document your own custom functions, write scripts, and handle errors. Lastly, you’ll learn to find and use modules in the Python Standard Library and other third-party libraries.
- Subjects:
- Computing
- Keywords:
- Python (Computer program language)
- Resource Type:
- MOOC