人工智能基礎(chǔ)（高中版）教材補充和資源分享之二 機器人學(xué)矩陣（人工智能基礎(chǔ) 高中版pdf）

關(guān)于書中對應(yīng)各章節(jié)的要點，稍晚些時候補充，這里先給出湯曉鷗教授和陳玉琨教授的一些點評，我個人讀后收獲非常大。

.參考：https://www.ithome.com/html/it/345470.htm

.參考：http://epaper.oeeee.com/epaper/H/html/2017-01/04/content_749.htm

文末附機器人學(xué)習(xí)能力矩陣，包括：小學(xué)、初中、高中、學(xué)士、碩士、博士。

補充知識和閱讀材料：

--一份為高中生準(zhǔn)備的機器學(xué)習(xí)與人工智能入門指南--

翻譯版鏈接：https://blog.csdn.net/dqcfkyqdxym3f8rb0/article/details/79347385

英文原版：https://github.com/kjaisingh/high-school-guide-to-machine-learning

Being a high schooler myself and having studied Machine Learning and Artificial Intelligence for a year now, I believe that there fails to exist a learning path in this field for High School students. This is my attempt at creating one.

A guide for High School students to learning Machine Learning and Artificial Intelligence

Being a high schooler myself and having studied Machine Learning and Artificial Intelligence for a year now, I believe that there fails to exist a learning path in this field?for High School students. This is my attempt to create one.

Over the past few months, I've tried to spend a couple of hours every day understanding this field, be it watching Youtube videos or undertaking projects. I've been guided by older peers who've had far more experience than me, and now feel that I have ample experience to share my insights.

All the information that I have compiled in this guide is intended for high schoolers wishing to excel in this up and coming field. It is intended to be followed chronologically, and unlike most guides/learning paths that I've come across,?doesn't require an understanding?of linear algebra, partial derivatives and other complex mathemathical concepts which one cannot find in their high school syllabuses.

If you work through this path on a regular basis, I believe that you could get to a pretty high level in about three months. However, this learning path does provide content that can keep you learning for the rest of your high school stay.

So, lets get to it.

1. Learning Python, which you will code your algorithms in.

I strongly suggest Python for this - not only is it extremely easy to learn, it supports pretty much any good library used in Machine Learning. While R is useful, I just find that Python in general is far more suitable for high school students. Besides basic programming, for Machine Learning in particular, the libraries that are most useful are Numpy, Pandas and Matplotlib.

For those of you who have never coded before, I suggest going to a course provided by the University of Toronto (one of the best universities for ML/AI right now). It will take you a few weeks, but its well worth your time - most of the knowledge you gain through this course can be applied to any other programming language, the only difference being the syntax. The course is free, and can be found at?https://www.coursera.org/learn/learn-to-program?siteID=SAyYsTvLiGQ-rs4V8qoewjp3oL7Nr.r_Fw&utm_content=10&utm_medium=partners&utm_source=linkshare&utm_campaign=SAyYsTvLiGQ#

For those of you who have coding experience in a language besides Python, just skim through this tutorial for syntax - it won't take you more than a day:?https://www.tutorialspoint.com/python/python_basic_syntax.htm

Now, after you've learnt the basics of Python, you need to understand the first two libraries I was talking about (Matplotlib can come later). Numpy arrays and Pandas are used for moving around and modifying the data you use, while Matplotlib is used to visualize this data through graphs and diagrams. The following courses together shouldn't take more than a couple of days:

Numpy:?http://cs231n.github.io/python-numpy-tutorial/

Pandas:?https://pandas.pydata.org/pandas-docs/stable/10min.html

Great! Now you should be set in the core programming needed to learn Machine Learning and Artificial Intelligence.

2. Getting into the basics of Machine Learning.

If there's one universal course for Machine Learning, it has to be Andrew Ng's course. This course is nothing short of brilliant, though for high school students, it may seem slightly challenging at times, as it refers to concepts such as partial derivatives (though these aren't required to understand the course). I found it beneficial to re-watch some lectures in Weeks 3 to 5 - it may be a bit fast the first time around.

I encourage everyone to go through this and take notes, though doing the programming-related tutorials and exercises is not needed, as its done in Matlab, which (in my experience) is often too tough for high schoolers to grasp. But don't worry, we will be doing the very same (and far more advanced) algorithms in Python in just a short amount of time.

The link to this free course is:?https://www.coursera.org/learn/machine-learning

3. Learning an assortment of machine learning algorithms and understand how to implement them in real-world scenarios.

Now, understanding machine learning algorithms without the knowledge of university-level maths - this should be hard in theory, however, a team from Australia resolved this issue.

Kirill Eremenko and Hadelin de Ponteves - a pair from the SuperDataScience team - are absolutely fantastic at finding relevant ways to apply simple algorithms in real life. Furthermore, they go into a suitable amount of depth to understand the functionality of the algorithm, but without the complex mathics that a high school would not be able to understand.

Their course covers both Python and R, though you don't have to worry about R - simply go through the Python tutorials. Also, if you find that they are going a bit too slow, play this course at 1.25x speed (I did that and found it much better).

Their course is on Udemy, and is paid, though Udemy regularly has discounts of 90% or more on their courses. It can be found at?https://www.udemy.com/machinelearning/learn/v4/overview, and is usually around .

It covers everything from basic regression algorithms to deep and convolutional neural networks. If you wish to explore even more advanced areas, their Deep Learning course is offered at the end of the Machine Learning for a 90% discount. However, concepts in this second course may be a little advanced and lack proper documentation, since they are so new.

If you're unwilling to pay for this course, you can check out Google's free Deep Learning course at?https://www.udacity.com/course/deep-learning--ud730?or University of Michigan's free course at?https://www.coursera.org/learn/python-machine-learning. However, these are far from as well-rounded as the SuperDataScience team's courses.

For these courses, taking notes aren't a necessity - there are tons of 'algorithm cheat sheets' online, which offer a quick intution on how they work. This website (https://www.analyticsvidhya.com/blog/2017/02/top-28-cheat-sheets-for-machine-learning-data-science-probability-sql-big-data/) lists a few.

4. Explore, explore and explore.

Now, you've covered a wide range of machine learning concepts, and have learnt a vast amount of skills. Its time for you to independently use these on basic projects. I'd suggest going to Kaggle (https://www.kaggle.com/) or the UCI Machine Learning repository (http://archive.ics.uci.edu/ml/datasets.html), finding a dataset you have an interest in, and simply modelling some solutions to these. Play around with different algorithms, and try to optimize performance.

Ensure that the datasets you use are simple and clean in nature - they shouldn't require too much pre-processing or modifying. Some easy dataset (off the top of my head) are the Iris, Wine, Breast Cancer Wisconsin, Autism Screening, Congress Voting, Handwritten Digits MNIST and Fashion MNIST ones.

If you ever come across a road block, Stack Overflow (https://stackoverflow.com/) is your best friend - they have an answer to almost any question that you'd have. If it doesn't, just post one - you should get replies within a couple of hours!

There's nothing much more I need to say here - when you find that you've become comfortable with the whole modelling process, feel free to move on!

5. Find an area of particular interest, and dive deeper.

Now you've got a great and broad understanding of all the basics. However, there's only a limit to what you can do with this. Thus, I suggest you find an area of interest in the broad field of Machine Learning, and look deeper into it. You probably won't have time to become experts in all of these in your high school tenure, but try and conquer one, if not two.

I'll list some possible areas, but before you begin one of these, understand what it is you're getting into. A simple Youtube search for a high-level explanation will give you all you need.

Computer Vision: Probably the hottest field in Machine Learning/AI right now - making computers see and understand things using a special type of neural network. Stanford publishes their course in this online (http://cs231n.stanford.edu/), with lectures, course notes and assignments available online. Go through this, though if the math is too complicated, don't worry - the course is simply to deepen your knowledge. Alternatively, you could look to OpenCV, a computer vision library that does a lot of the complex stuff for you. A great tutorial can be found at?https://www.youtube.com/watch?v=Z78zbnLlPUA&list=PLQVvvaa0QuDdttJXlLtAJxJetJcqmqlQq. Once you're done with these, look at more advanced image datasets on Kaggle and UCI, or even enter Kaggle Competitions.

Natural Language Processing: Understanding how computers learn to speak is also a prominent topic today. Once again, Stanford offers a great course thats online (http://web.stanford.edu/class/cs224n/). If you don't understand some of the Math concepts, don't worry, just gain an understanding of how this field works. For implementations, you could undertake this Udemy course:?https://www.udemy.com/data-science-natural-language-processing-in-python/. However, you could alternatively go through some of well-known Machine Learner Siraj Raval's videos (such as?https://www.youtube.com/watch?v=9zhrxE5PQgY). One you've done these, try undertaking simple, well-known projects like building a chatbot, sentiment analysis or creating lyrics to a song - simple Youtube searches should help you out.

Reinforcement Learning: This domain focuses on how machines learn to act in a particular way, and its most popular application is in the field of video games. Siraj Raval again has a pretty good playlist on this (https://www.youtube.com/watch?v=i_McNBDP9Qs&list=PL2-dafEMk2A5FZ-MnPMpp3PBtZcINKwLA). David Silver's UCL course is great, though beginners may find it a bit tricky -?http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html. Once you're done with these, its pretty logical to just start downloading base projects from online, and adding an element of Artificial Intelligence to modify how the agents they act. Simple walkthroughs can again be found via a simple Youtube search.

Data Science: This field is a budding domain with tons of exciting job oppurtunities. I suggest undertaking either SuperDataScience's paid course (https://www.udemy.com/datascience/) or UC San Diego's Python-based free course (https://www.edx.org/course/python-data-science-uc-san-diegox-dse200x), though you should find a specific learning path for data science with a simple Google search. You must also learn SQL (https://www.khanacademy.org/computing/computer-programming/sql) as well as Matplotlib (https://www.youtube.com/watch?v=q7Bo_J8x_dw). The advantage in learning this at a student level is employability - I have friends in high school who've been offered data science internships, as the data gained from their work can instantly be monetized by companies. Its easy for companies to find a way to use a data scientist.

There are also areas like Representation Learning (used for recommendation systems), Adversial Networks (AI improving AI) and Genetic Algorithms (improving a solution to a problem in a way similar to natural evolution), but in my opinion, these are a stretch for most high school students. Do feel free to explore these if you have a particular passion for one of them, though they aren't as well covered as the other domains, largely due to the fact that they aren't currently monetized as much as the other fields.

BONUS (extremely important). Truly understand the field of Artificial Intelligence.

If you want to work in this field in the long run, its crucial to understand what it is about, groundbreaking discoveries and its implications on society. You should start doing things listed in this section as soon as you have the necessary understanding of how the technology works - I believe that this is after Section 4 of this learning path (as you start delving into an area of interest). This kind of information may not particularly help you when implementing algorithms, but its an impressive sign for universities or companies when their prospects are so knowledgeable in the field itself, rather than just the code.

There's a few things that a high schooler should do to deepen their general understanding of the field and make them more knowledgeable, which I'll list here:

Start reading research papers: They really aren't as challenging as they sound. A good portion of them can be almost completely comprehended with a high school Math level. If you ever come across one you don't understand, just put it down - theres more than enough alternatives. This link (https://www.kdnuggets.com/2017/04/top-20-papers-machine-learning.html) offers a host of great papers, though after you finish those, this (http://www.jmlr.org/papers/) offers the most up-to-date list - simply read ones you're interested in or related to your area of 'expertise' from section 5.

Listen to the pioneers: People like Andrew Ng, Ian Goodfellow and Yann LeCunn are regularly interviewed, and give the perspective of engineers of this field on the subject of AI. This Youtube channel (https://www.youtube.com/user/Maaaarth/videos) gathers the best of these talks, and compiles them into a central resource - watch one a night, and I guarantee that you'll feel like an expert within weeks.

Stay up-to-date with the field: Wired is one of the best platforms for anyone interested in tech. It publishes multiple AI-related stories every day, which can be found at?https://www.wired.com/tag/artificial-intelligence/. Its simply a great way to understand the trends of the time. Alternatively, subsribe to TechCrunch's Facebook Messenger bot - it often has interested AI-related articles, and prompts you every day.

Understand the implications: There's no better way to do this than listening to TED talks. Their speakers are extremely knowledgeable in the field, and there is an increasing emphasis on AI in their speeches. A host of videos can be found at?https://www.youtube.com/user/TEDtalksDirector/videos

The Philosophy: AI has its supporters and its opposers. The philosphy behind it, however, is intriguing. My favourite books that explore this area, and are suitable for High School students, include 'How to Create a Mind' by Ray Kurzweil and 'Life 3.0' by Max Tegmark (http://s3.amazonaws.com/arena-attachments/1446178/cffa5ebc74cee2b1edf58fa9a5bbcb1c.pdf?1511265314) - do try and read these.

Contributing: If you're the kind of person who likes to learn from others experience, check out the Artificial Intelligence & Deep Learning Facebook group. Alternatively, if you prefer conversations, check out Reddit's thread on AI at?https://www.reddit.com/r/artificial/.

Delve into the math: Yes, you do need university level math skills, but if you're an extremeley strong math student, there's nothing stopping you from taking some online courses. This quora thread (https://www.quora.com/How-do-I-learn-mathematics-for-machine-learning) has some great resources that you should check out. 3Blue1Brown (https://www.youtube.com/channel/UCYO_jab_esuFRV4b17AJtAw/playlists) is a famous name in the community too, as his Youtube videos are fantastic for learning the maths behind some of these more complicated concepts.

Conclusion

I wish everyone the best of luck in undertaking this learning path. I've heard too many people say Machine Learning and Artificial Intelligence is too complicated to learn as a high school student to not write this - with a well-paved learning path, it can be done by anyone. Its just that due to the field being so new and generally thought of as a graduate field of study, theres a lack of one for high school students.

If anyone has additions, suggestions, queries or feedback, feel free to write to me @?kj.jaisingh@gmail.com.

--機器人方向--

MIT Zero Robotics中學(xué)計劃：http://zerorobotics.mit.edu/ms/

--NASA機器人學(xué)矩陣--

涵蓋了從幼兒園到博士的全部內(nèi)容。五星推薦。https://robotics.nasa.gov/edu/matrix.php

----

Educational Robotics Matrix

Check back often as this list always continues to develop. If you have any suggestions for additional content, or to report broken links, please direct your e-mail to?arc-rap@mail.nasa.gov

Interested in how the National Science Education Standards relate to robotics?

Find out at?BotBalls' Website!

Find National Standards for the following subjects:

Math,?Science,?Engineering,?Technology?(ITEEA?and?ISTE),?Language Arts,?Social Studies

--具體內(nèi)容如下--

Curriculum

Competitions

Internships

K to 5th

Squeakland

Let younger children try their hand at programming.

+ More information

ImagiBotics

Robotics classroom activities, articles, and interviews at Imagiverse

+ More information

Pre-K-12 Engineering

Standards-aligned engineering activities for grades Pre-K to 12th Grade.

+ More information

Make Your Own Rover

Eleven activities designed to help students learn about robotics.

+ More information

Classroom Robotics on the Web

Lessons, materials, and rubrics developed in a Texas classroom using RoboLab.

+ More information

Imagine Mars

Students explore their own community and decide which arts, scientific and cultural elements will be important as they develop an ideal community on Mars.

+ More information

Build a Nanorover

Learn how to make your own balloon-powered nanorover!

+ More information

Robotics Activities

Design, build and program a real robot using sensors, controllers and actuators.

+ More information

Robotics Starter Kits for Educators

A guide to different robotics kits.

+ More information

RoboCupJunior

+ More information

Trinity Fire-Fighting Robot Contest

+ More information

FIRST??LEGO? League Junior

Designed to introduce STEM concepts to kids ages 6 to 10

+ More information

Robofest

Robofest is an annual autonomous robotics competition focusing on learning STEM for students in grades 5 - 12.

+ More information

FIRST??LEGO? League

Immerses kids in real-world science and technology challenges.

+ More information

Storming Robots

Storming Robots offers classes for those interested in participating in robotics competitions, robotics technology clubs, computer programming classes, etc.

+ More information

EARLY Robotics

Engineering And Robotics Learned Young (EARLY) is a program that exposes our youth to engineering. EARLY provides 7 to 12 year olds the opportunity to participate in a robotics competition every fall and spring.

+ More information

ID Tech Camps

+ More information

Space Camp

+ More information

6th to 8th

ImagiBotics

Robotics classroom activities, articles, and interviews at Imagiverse

+ More information

Pre-K-12 Engineering

Standards-aligned engineering activities for grades Pre-K to 12th Grade.

+ More information

Make Your Own Rover

Eleven activities designed to help students learn about robotics.

+ More information

Imagine Mars

Students explore their own community and decide which arts, scientific and cultural elements will be important as they develop an ideal community on Mars.

+ More information

Robotics Activities

Design, build and program a real robot using sensors, controllers and actuators.

+ More information

Robotics Starter Kits for Educators

A guide to different robotics kits.

+ More information

Robotics Academy Camp-on-a-Disk

+ More information

Botball and Computer Science Curriculum

Curricular materials to promote hands-on learning.

+ More information

Multi-unit Robotics Curriculum

Give students meaningful exercises that introduce or reinforce applied physics and mathematics concepts.

+ More information

BotBall

+ More information

RoboCupJunior

+ More information

Trinity Fire-Fighting Robot Contest

+ More information

Battlebots IQ

+ More information

Robofest

Robofest is an annual autonomous robotics competition focusing on learning STEM for students in grades 5 - 12.

+ More information

FIRST??LEGO? League

Immerses kids in real-world science and technology challenges.

+ More information

Storming Robots

Storming Robots offers classes for those interested in participating in robotics competitions, robotics technology clubs, computer programming classes, etc.

+ More information

ID Tech Camps

+ More information

Space Camp

+ More information

9th to 12th

Pre-K-12 Engineering

Standards-aligned engineering activities for grades Pre-K to 12th Grade.

+ More information

Robotics Starter Kits for Educators

A guide to different robotics kits.

+ More information

Robotics Academy Camp-on-a-Disk

+ More information

Botball and Computer Science Curriculum

Curricular materials to promote hands-on learning.

+ More information

Multi-unit Robotics Curriculum

Give students meaningful exercises that introduce or reinforce applied physics and mathematics concepts.

+ More information

IntelliBrain-Bot

Java-programmable educational robot.

+ More information

GEARS Educational Systems

Robotics platform with projects and suggested curriculum.

+ More information

Battlebots IQ

Educational lessons about robotics.

+ More information

Robotic Autonomy Activities

A series of exercises intended to explore principles of autonomous robotics.

+ More information

Robotics Programming

Simulate robotics missions and learning BASIC programming.

+ More information

Robotics Technology Curriculum

Select activities to suit the learner from elementary to advanced and manual robot control to computer program modes.

+ More information

Stamps in Class

Introduction to topics such as microcontrollers, analog and digital, and advanced robotics.

+ More information

EST Foundations

The EST Foundations curriculum is comprised of project based lesson plans to introduce students to engineering. Students will explore the new product development process, how marketing impacts engineering, and careers in engineering and science. There are eleven fully developed one-week modules.

+ More information

BotBall

+ More information

RoboCupJunior

+ More information

Trinity Fire-Fighting Robot Contest

+ More information

Battlebots IQ

+ More information

BEST Robotics

+ More information

FIRST? Robotics Competition

+ More information

Robofest

Robofest is an annual autonomous robotics competition focusing on learning STEM for students in grades 5 - 12.

+ More information

Storming Robots

Storming Robots offers classes for those interested in participating in robotics competitions, robotics technology clubs, computer programming classes, etc.

+ More information

FIRST? Tech Challenge

+ More information

VEX Robotics Competition

+ More information

NASA Student Opportunities

+ More information

ID Tech Camps

+ More information

Robotics Boot Camp

At San Jose City College

+ More information

NASA Summer High School Apprenticeship Research Program (SHARP)

+ More information

NASA MSFC Robotics Academy

+ More information

BA/BS

List of universities offering robotics programs

+ More information

Autonomous Multirobot Systems

Key issues and current research in autonomous multirobot systems.

+ More information

General Robotics Course

Overview of robotics in practice and research.

+ More information

CMU Undergraduate Minor in Robotics

Focus on robotics.

+ More information

Universities

Study and research programs in robotics.

+ More information

Trinity Fire-Fighting Robot Contest

+ More information

Battlebots IQ

+ More information

Collaborative Research Experience

For women in undergraduate computer science and engineering

+ More information

RoboCup

+ More information

VEX Robotics Competition

+ More information

NASA Student Opportunities

+ More information

NASA Jobs

+ More information

Storming Robots Internship

+ More information

iD Tech Camps Jobs

+ More information

NASA MSFC Robotics Academy

+ More information

MA/MS

List of universities offering robotics programs

+ More information

Autonomous Multirobot Systems

Key issues and current research in autonomous multirobot systems.

+ More information

General Robotics Course

Overview of robotics in practice and research.

+ More information

Carnegie Mellon University Robotics Institute

Graduate education in robotics.

+ More information

Universities

Study and research programs in robotics.

+ More information

Trinity Fire-Fighting Robot Contest

+ More information

RoboCup

+ More information

NASA Student Opportunities

+ More information

NASA Jobs

+ More information

Planetary Science Summer School

+ More information

Ph.D

List of universities offering robotics programs

+ More information

Autonomous Multirobot Systems

Key issues and current research in autonomous multirobot systems.

+ More information

General Robotics Course

Overview of robotics in practice and research.

+ More information

Carnegie Mellon University Robotics Institute

Graduate education in robotics.

+ More information

Universities

Study and research programs in robotics.

+ More information

Trinity Fire-Fighting Robot Contest

+ More information

RoboCup

+ More information

NASA Student Opportunities

+ More information

NASA Jobs

+ More information

Planetary Science Summer School

+ More information

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機器人世界與機器人操作系統(tǒng)（ROS）：https://www.edx.org/course/hello-real-world-with-ros-robot-operating-system

你有興趣采取機器人技術(shù)的第一步嗎？你是否尋求切實可行的方法，并希望通過這樣做來學(xué)習(xí)？加入我們的課程，學(xué)習(xí)如何使用ROS編寫完整的真實世界機器人系統(tǒng)！

機器人操作系統(tǒng)（ROS）使您能夠通過訪問大量開源軟件和工具來快速構(gòu)建機器人應(yīng)用程序。多年來，ROS已經(jīng)成為機器人專家的必備工具。一個大型社區(qū)圍繞著ROS，工業(yè)用戶在這些工具的開發(fā)中已經(jīng)有了大量的投入。

用于操縱，感知和導(dǎo)航的許多新的高級機器人功能都是使用ROS開發(fā)的。像空客和波音這樣的公司正在使用ROS來處理他們的一些應(yīng)用。代爾夫特理工大學(xué)的代爾夫特機器人系統(tǒng)團隊在2016年亞馬遜機器人挑戰(zhàn)賽上贏得了兩項挑戰(zhàn)，其中使用ROS開發(fā)的機器人。

在本課程中，您將學(xué)習(xí)使用不同的ROS工具來創(chuàng)建一個完整的機器人應(yīng)用程序。您將在基于物理學(xué)的仿真引擎Gazebo上使用您自己的獨立Ubuntu-Linux安裝以及工業(yè)和移動機器人。您將學(xué)習(xí)編程和配置基本的機器人任務(wù)，例如拾取和放置對象，并瀏覽障礙物。然后，您將整合所有這些知識，用兩個機器人手臂和一個移動機器人構(gòu)建工業(yè)生產(chǎn)線。

最好的最終項目將在代爾夫特的工業(yè)示范設(shè)施中的真實機器人上進行測試！

你準(zhǔn)備好讓你的機器人用ROS來向你的（真實）世界說“你好”嗎？

本課程的課程資料屬于代爾夫特理工大學(xué)版權(quán)所有，并根據(jù)知識共享署名 - 非商業(yè)性使用 - 相同方式（CC-BY-NC-SA）4.0國際許可證授權(quán)。

你會學(xué)到什么

如何使用ROS通信工具（主題，服務(wù)，動作）在功能模塊之間交換信息

使用機器人可視化并創(chuàng)建自定義環(huán)境

機器人環(huán)境和導(dǎo)航與移動機器人的映射

如何用工業(yè)機器人手臂實現(xiàn)拾放功能

用狀態(tài)機設(shè)計一個完整的機器人應(yīng)用程序

課程綱要

----

稍后補充一些書籍資料pdf。

--機器人學(xué)習(xí)能力矩陣--

https://robotics.nasa.gov/edu/matrix.php

課程

比賽

實習(xí)

小學(xué)階段

Squeakland

讓年幼的孩子嘗試編程。

+更多信息

Imagiverse

機器人課堂活動，文章和Imagiverse采訪

+更多信息

Pre-K-12工程

標(biāo)準(zhǔn)調(diào)整工程活動適用于Pre-K至12年級的工程活動。

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制作你自己的Rover

Eleven 11個旨在幫助學(xué)生學(xué)習(xí)機器人技術(shù)的活動。

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Web上的教室機器人

使用RoboLab在德克薩斯州的教室中開發(fā)課程，材料和標(biāo)記。

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想象一下火星

學(xué)生探索自己的社區(qū)，并決定在火星上發(fā)展理想社區(qū)時，哪些藝術(shù)，科學(xué)和文化元素將變得重要。

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建立一個Nanorover

學(xué)習(xí)如何使你自己的氣球動力nanorover！

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機器人活動

使用傳感器，控制器和執(zhí)行器設(shè)計，構(gòu)建和編程一個真實的機器人。

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RoboCupJunior

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三位一體消防機器人大賽

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FIRST?LEGO?League?Junior

旨在將STEM概念引入6至10歲兒童

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Robofest

Robofest是一個年度自主機器人競賽，專注于為5 - 12年級的學(xué)生學(xué)習(xí)STEM。

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FIRST??LEGO?聯(lián)賽

沉浸在真實世界中的科學(xué)和技術(shù)挑戰(zhàn)的孩子。

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攻堅機器人

攻堅機器人為那些有興趣參加機器人競賽，機器人技術(shù)俱樂部，計算機編程課程等的人提供課程

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EARLY Robotics

Engineering and Robotics Learned Young（EARLY）是一個讓我們的年輕人接觸工程的計劃。EARLY提供7至12歲的孩子參加每年秋季和春季的機器人比賽的機會。

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ID Tech Camps

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空間營

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初中階段

Imagiverse

機器人課堂活動，文章和Imagiverse采訪

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Pre-K-12工程

標(biāo)準(zhǔn)調(diào)整工程活動適用于Pre-K至12年級的工程活動。

+更多信息

制作你自己的Rover

Eleven 11個旨在幫助學(xué)生學(xué)習(xí)機器人技術(shù)的活動。

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想象一下火星

學(xué)生探索自己的社區(qū)，并決定在火星上發(fā)展理想社區(qū)時，哪些藝術(shù)，科學(xué)和文化元素將變得重要。

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機器人活動

使用傳感器，控制器和執(zhí)行器設(shè)計，構(gòu)建和編程一個真實的機器人。

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機器人學(xué)院夏令營

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Botball和計算機科學(xué)課程

提供實踐學(xué)習(xí)的課程材料。

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多單元機器人課程為

學(xué)生提供有意義的練習(xí)，介紹或強化應(yīng)用的物理和數(shù)學(xué)概念。

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BotBall

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RoboCupJunior

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三位一體消防機器人大賽

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Battlebots IQ

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Robofest

Robofest是一個年度自主機器人競賽，專注于為5 - 12年級的學(xué)生學(xué)習(xí)STEM。

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FIRST??LEGO?聯(lián)賽

沉浸在真實世界中的科學(xué)和技術(shù)挑戰(zhàn)的孩子。

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攻堅機器人

攻堅機器人為那些有興趣參加機器人競賽，機器人技術(shù)俱樂部，計算機編程課程等的人提供課程

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ID Tech Camps

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空間營

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高中階段

Pre-K-12工程

標(biāo)準(zhǔn)調(diào)整工程活動適用于Pre-K至12年級的工程活動。

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機器人學(xué)院夏令營

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Botball和計算機科學(xué)課程

提供實踐學(xué)習(xí)的課程材料。

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多單元機器人課程為

學(xué)生提供有意義的練習(xí)，介紹或強化應(yīng)用的物理和數(shù)學(xué)概念。

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IntelliBrain-Bot

Java可編程教育機器人。

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GEARS教育系統(tǒng)

帶有項目和建議課程的機器人平臺。

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Battlebots IQ

關(guān)于機器人的教育課程。

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機器人自主活動

旨在探索自主機器人原理的一系列練習(xí)。

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機器人編程

模擬機器人任務(wù)和學(xué)習(xí)BASIC編程。

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機器人技術(shù)課程

選擇適合學(xué)習(xí)者從初級到高級和手動機器人控制到計算機程序模式的活動。

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課堂上的郵票

介紹諸如微控制器，模擬和數(shù)字以及高級機器人等主題。

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EST基礎(chǔ)

EST基礎(chǔ)課程包括基于項目的課程計劃，向?qū)W生介紹工程。學(xué)生將探索新產(chǎn)品開發(fā)過程，營銷如何影響工程，以及工程和科學(xué)職業(yè)。有11個完全開發(fā)的一周模塊。

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BotBall

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RoboCupJunior

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三位一體消防機器人大賽

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Battlebots IQ

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BEST Robotics

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FIRST?機器人競賽

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Robofest

Robofest是一個年度自主機器人競賽，專注于為5 - 12年級的學(xué)生學(xué)習(xí)STEM。

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攻堅機器人

攻堅機器人為那些有興趣參加機器人競賽，機器人技術(shù)俱樂部，計算機編程課程等的人提供課程

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FIRST?技術(shù)挑戰(zhàn)

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VEX機器人大賽

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美國宇航局學(xué)生機會

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ID Tech Camps

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圣何塞城市學(xué)院機器人學(xué)新手訓(xùn)練營

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美國宇航局夏季高中學(xué)徒研究計劃（夏普）

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美國宇航局MSFC機器人學(xué)院

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學(xué)士本科階段

提供機器人程序的大學(xué)列表

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自主多機器人系統(tǒng)自主多機器人系統(tǒng)的

關(guān)鍵問題和當(dāng)前研究。

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一般機器人課程

機器人在實踐和研究中的概述。

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CMU機器人本科專業(yè)

重點研究機器人技術(shù)。

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大學(xué)

機器人學(xué)習(xí)和研究項目。

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三位一體消防機器人大賽

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Battlebots IQ

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合作研究經(jīng)歷

針對本科生計算機科學(xué)與工程專業(yè)的女生

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RoboCup

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VEX機器人大賽

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美國宇航局學(xué)生機會

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美國宇航局喬布斯

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強攻機器人實習(xí)

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iD Tech Camps Jobs

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美國宇航局MSFC機器人學(xué)院

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碩士研究生階段

提供機器人程序的大學(xué)列表

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自主多機器人系統(tǒng)自主多機器人系統(tǒng)的

關(guān)鍵問題和當(dāng)前研究。

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一般機器人課程

機器人在實踐和研究中的概述。

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卡內(nèi)基梅隆大學(xué)機器人研究所機器人

研究生教育。

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大學(xué)

機器人學(xué)習(xí)和研究項目。

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三位一體消防機器人大賽

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RoboCup

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美國宇航局學(xué)生機會

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美國宇航局喬布斯

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行星科學(xué)暑期學(xué)校

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博士研究生階段

提供機器人程序的大學(xué)列表

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自主多機器人系統(tǒng)自主多機器人系統(tǒng)的

關(guān)鍵問題和當(dāng)前研究。

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一般機器人課程

機器人在實踐和研究中的概述。

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卡內(nèi)基梅隆大學(xué)機器人研究所機器人

研究生教育。

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大學(xué)

機器人學(xué)習(xí)和研究項目。

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三位一體消防機器人大賽

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RoboCup

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美國宇航局學(xué)生機會

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美國宇航局喬布斯

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行星科學(xué)暑期學(xué)校

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5G教育 機器人

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