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With the help of modules, one can assemble their own courses. Courses can be designed individually or for a class with multiple students.
One of the main tools to export such courses is bookmanager which you can find out about at
https://pypi.org/project/cyberaide-bookmanager/
https://github.com/cyberaide/bookmanager
This course introduces the REU students to various topics in Intelligent Systems Engineering. The course was taught in Summer 2021.
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Rstudio with Git and GitHub Slides |
Python is a great languge for doing data science and AI, a comprehensive list of features is available in book form. Please note that when installing Python, you always want to use a venv as this is best practice.
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Introduction to Python (ePub) (PDF) |
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Installation of Python — June 7th, 2021 (AM) |
Update to the Video:
Best practices in Python recommend to use a Python venv. This is pretty easy to do and creates a separate Python environment for you so you do not interfere with your system Python installation. Some IDEs may do this automatically, but it is still best practice to install one and bind the IDE against it. To do this:
Download Python version 3.9.5 just as shown in the first lecture.
After the download you do an additional step as follows:
on Mac:
python3.9 -m venv ~/ENV3
source ~/ENV/bin/activate
you need to do the source every time you start a new window or on mac ass it to .zprofile
on Windows you first install gitbash and do all yuour terminal work from gitbash as this is more Linux-like. In gitbash, run
python -m venv ~/ENV3
~/ENV/Script/activate
In case you like to add it to gitbash, you can add the source line to .bashrc and/or .bash_profile
In case you use VSCode, you can also do it individually in a directory where you have your code.
cd TO YOUR DIR; python3.9 -m venv .cd TO YOUR DIR; python -m venv .Then start VSCode in the directory and it will ask you to use this venv. However, the global ENV3 venv may be better and you cen set your interpreter to it.
On Pycharm we recommend you use the ENV3 and set the clobal interpreter
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Jupyter Notebooks — June 7th, 2021 (PM): This lecture provides an introduction to Jupyter Notebooks using Visual Studio as IDE. |
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Video: Github |
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Video-Github 2 — June 8th, 2021 (PM): In this lecture the student can learn how to create a project on RStudio and link it with a repository on GitHub to commit, pull and push the code from RStudio. |
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Slides: This introduction to Python cover the different data type, how to convert type of variable, understand and create flow control usign conditional statements. |
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Slides — June 10th, 2021 (PM): String, Numbers, Booleans Flow of control Using If statements |
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Slides: String, Numbers, Booleans Flow of control Using If statements (2) |
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Python Exercises - Lab 2 |
The first exercise will require a simple for loop, while the second is more complicated, requiring nested for loops and a break statement.
General Instructions: Create two different files with extension .ipnyb, one for each problem. The first file will be named factorial.ipnyb which is for the factorial problem, and the second prime_number.ipnyb for the prime number problem.
Write a program that can find the factorial of any given number. For example, find the factorial of the number 5 (often written as 5!) which is 12345 and equals 120. Your program should take as input an integer from the user.
Note: The factorial is not defined for negative numbers and the factorial of Zero is 1; that is 0! = 1.
You should
A Prime Number is a positive whole number, greater than 1, that has no other divisors except the number 1 and the number itself. That is, it can only be divided by itself and the number 1, for example the numbers 2, 3, 5 and 7 are prime numbers as they cannot be divided by any other whole number. However, the numbers 4 and 6 are not because they can both be divided by the number 2 in addition the number 6 can also be divided by the number 3.
You should write a program to calculate prime number starting from 1 up to the value input by the user.
You should
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Video — June 11th, 2021 (AM): Motivation for the REU: Data is Driven Everything |
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Slides: Motivation for the REU: Data is Driven Everything |
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Slides: Descriptive Statistic |
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Slides: Probability |
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Video — June 28th, 2021 (AM): Working on GitHUb Template and Mendeley references management |
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Slides: Data Science Tools |
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Video — June 14th, 2021 (AM): Numpy |
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Video — June 14th, 2021 (PM): Pandas data frame |
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Video — June 15th, 2021 (AM): Web data mining |
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Video — June 15th, 2021 (PM): Pandas IO |
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Video — June 16th, 2021 (AM): Pandas |
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Video: Pycharm Installation and Virtual Environment setup — June 18th, 2021 (AM) |
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Video: This lecture the student can learn the different applications of Matrix Operation using images on Python. — June 21st, 2021 (AM) |
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Video: Data wrangling and Descriptive Statistic Using Python — June 21st, 2021 (AM) |
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Video: Data wrangling and Descriptive Statistic Using Python — June 22nd, 2021 (PM) |
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Video: FURY Visualization and Microsoft Lecture — June 25th, 2021 (PM) |
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Video: Instroduction to Probability — June 25th, 2021 (PM) |
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Video: Digital Twins and Virtual Tissue ussing CompuCell3D Simulating Cancer Somatic Evolution in nanoHUB — July 2nd, 2021 (AM) |
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Video: AI First Engineering: Learning material — June 25th, 2021 (AM) |
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Video: Adding content to your su21-reu repositories — June 17th, 2021 (PM) |
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Slides: AI First Engineering |
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Video: Datasets for Projects: Data world and Kaggle — June 29th, 2021 (AM) |
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Video: Datasets for Projects: Data world and Kaggle part 2 — June 29th, 2021 (PM) |
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Video: K-Means: Unsupervised model — June 30th, 2021 (AM) |
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Video: Support Vector Machine: Supervised model — July 2nd, 2021 (PM) |
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Slides: Support Vector Machine Supervised model. |
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Video: Neural Networks: Deep Learning Supervised model — July 6th, 2021 (AM) |
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Video: Neural Networks: Deep learning Model — July 6th, 2021 (AM) |
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Video: Data Visualization: Visualizaton for Data Science — July 7th, 2021 (AM) |
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Video: Convulotional Neural Networks: Deep learning Model — July 8th, 2021 (AM) |
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Video: Student Report Help with Introduction and Datasets — July 7th, 2021 (AM) |
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Video: Student Report Help with Introduction and Datasets — July 13th, 2021 (AM) |
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Video: Chemo-Preventive Effect of Vegetables and Fruits Consumption on the COVID-19 Pandemic — July 1st, 2021 (AM) |
This course introduces the students to AI-First Engineering Cybertraining we provide the following sections
As part of this class, we will be using a variety of sources. To simplify the presentation we provide them in a variety of smaller packaged material including books, lecture notes, slides, presentations and code.
Note: We will regularly update the course material, so please always download the newest version. Some browsers try to be fancy and cache previous page visits. So please make sure to refresh the page.
We will use the following material:
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Course Lectures. These meeting notes are updated weekly (Web) |
This course is built around the revolution driven by AI and in particular deep learning that is transforming all activities: industry, research, and lifestyle. It will a similar structure to The Big Data Class and the details of the course will be adapted to the interests of participating students. It can include significant deep learning programming.
All activities – Industry, Research, and Lifestyle – are being transformed by Artificial Intelligence AI and Big Data. AI is currently dominated by deep learning implemented on a global pervasive computing environment - the global AI supercomputer. This course studies the technologies and applications of this transformation.
We review Core Technologies driving these transformations: Digital transformation moving to AI Transformation, Big Data, Cloud Computing, software and data engineering, Edge Computing and Internet of Things, The Network and Telecommunications, Apache Big Data Stack, Logistics and company infrastructure, Augmented and Virtual reality, Deep Learning.
There are new “Industries” over the last 25 years: The Internet, Remote collaboration and Social Media, Search, Cybersecurity, Smart homes and cities, Robotics. However, our focus is Traditional “Industries” Transformed: Computing, Transportation: ride-hailing, drones, electric self-driving autos/trucks, road management, travel, construction Industry, Space, Retail stores and e-commerce, Manufacturing: smart machines, digital twins, Agriculture and Food, Hospitality and Living spaces: buying homes, hotels, “room hailing”, Banking and Financial Technology: Insurance, mortgage, payments, stock market, bitcoin, Health: from DL for pathology to personalized genomics to remote surgery, Surveillance and Monitoring: – Civilian Disaster response; Miltary Command and Control, Energy: Solar wind oil, Science; more data better analyzed; DL as the new applied mathematics, Sports: including Sabermetrics, Entertainment, Gaming including eSports, News, advertising, information creation and dissemination, education, fake news and Politics, Jobs.
We select material from above to match student interests.
Students can take the course in either software-based or report-based mode. The lectures with be offered in video form with a weekly discussion class. Python and Tensorflow will be main software used.
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Introduction to AI-Driven Digital Transformation (Web) |
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A Gentle Introduction to Google Colab (Web) |
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A Gentle Introduction to Python on Google Colab (Web) |
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MNIST Classification on Google Colab (Web) |
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MNIST-MLP Classification on Google Colab (Web) |
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MNIST-RNN Classification on Google Colab (Web) |
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MNIST-LSTM Classification on Google Colab (Web) |
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MNIST-Autoencoder Classification on Google Colab (Web) |
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MNIST with MLP+LSTM Classification on Google Colab (Web) |
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Distributed Training with MNIST Classification on Google Colab (Web) |
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PyTorch with MNIST Classification on Google Colab (Web) |
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Piazza. The link for all those that participate in the IU class to its class Piazza. |
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Scientific Writing with Markdown (ePub) (PDF) |
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Git Pull Request. Here you will learn how to do a simple git pull request either via the GitHub GUI or the git command line tools |
This course does not require you to do much Linux. However, if you do need it, we recommend the following as starting point listed
The most elementary Linux features can be learned in 12 hours. This includes bash, editor, directory structure, managing files. Under Windows, we recommend using gitbash, a terminal with all the commands built-in that you would need for elementary work.
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Introduction to Linux (ePub) (PDF) |
Older versions of the material are available at
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Lecture Notes 2020 (ePub) (PDF) |
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Big Data Applications (Nov. 2019) (ePub) (PDF) |
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Big Data Applications (2018) (ePub) (PDF) |
You can contribute to the material with useful links and sections that you find. Just make sure that you do not plagiarize when making contributions. Please review our guide on plagiarism.
This course does not require a sophisticated computer. Most of the things can be done remotely. Even a Raspberry Pi with 4 or 8GB could be used as a terminal to log into remote computers. This will cost you between $50 - $100 dependent on which version and equipment. However, we will not teach you how to use or set up a Pi or another computer in this class. This is for you to do and find out.
In case you need to buy a new computer for school, make sure the computer is upgradable to 16GB of main memory. We do no longer recommend using HDD’s but use SSDs. Buy the fast ones, as not every SSD is the same. Samsung is offering some under the EVO Pro branding. Get as much memory as you can effort. Also, make sure you back up your work regularly. Either in online storage such as Google, or an external drive.
We present here the project guidelines
All students of this class are doing a software project. (Some of our classes allow non software projects)
The final project is 50% of grade
All projects must have a well-written report as well as the software component
We must be able to run software from class GitHub repository. To do so you must include an appendix to your project report describing how to run your project.
There are a useful set of example projects submitted in previous classes
In this class you do not have the option to work on a joint report, however you can collaborate.
Note: all reports and projects are open for everyone as they are open source.
The major deliverable of the course is a software project with a report. The project must include a programming part to get a full grade. It is expected that you identify a suitable analysis task and data set for the project and that you learn how to apply this analysis as well as to motivate it. It is part of the learning outcome that you determine this instead of us giving you a topic. This topic will be presented by student in class April 1.
It is desired that the project has a novel feature in it. A project that you simply reproduce may not recieve the best grade, but this depends on what the analysis is and how you report it.
However “major advances” and solving of a full-size problem are not required. You can simplify both network and dataset to be able to complete project. The project write-up should describe the “full-size” realistic problem with software exemplifying an instructive example.
One goal of the class is to use open source technology wherever possible. As a beneficial side product of this, we are able to distribute all previous reports that use such technologies. This means you can cite your own work, for example, in your resume. For big data, we have more than 1000 data sets we point to.
Comments on Example Projects from previous classes
Warning: Please note that we do not make any quality assumptions to the published papers that we list here. It is up to you to identify outstanding papers.
Warning: Also note that these activities took place in previous classes, and the content of this class has since been updated or the focus has shifted. Especially chapters on Google Colab, AI, DL have been added to the course after the date of most projects. Also, some of the documents include an additional assignment called Technology review. These are not the same as the Project report or review we refer to here. These are just assignments done in 2-3 weeks. So please do not use them to identify a comparison with your own work. The activities we ask from you are substantially more involved than the technology reviews.
Plagiarism is of course not permitted. It is your responsibility to know what plagiarism is. We provide a detailed description book about it here, you can also do the IU plagiarism test to learn more.
All project reports must be provided in github.com as a markdown file.
All images must be in an images directory. You must use proper
citations. Images copied from the Internet must have a citation in the
Image caption. Please use the IEEE citation format and do not use
APA or harvard style. Simply use fotnotes in markdown but treat them as
regular citations and not text footnotes (e.g. adhere to the IEEE rules).
All projects and reports must
be checked into the Github repository. Please take a look at the example we created for you.
The report will be stored in the github.com.
./project/index.md
./project/images/mysampleimage.png
Length of Project Report
Software Project Reports: 2500 - 3000 Words.
Given next are links to collections of datasets that may be of use for homework assignments or projects.
[https://www.data.gov/] [https://github.com/caesar0301/awesome-public-datasets]
[https://www.quora.com/Where-can-I-find-large-datasets-open-to-the-public]
Why you should not just paste and copy into the GitHub GUI?
We may make comments directly in your markdown or program files. If you just paste and copy you may overlook such comments. HEns only paste and copy small paragraphs. If you need to. The best way of using github is from commandline and using editors such as pycharm and emacs.
I like to do a project that relates to my company?
Can I use Word or Google doc, or LaTeX to hand in the final document?
No. you must use github.com and markdown.
Please note that exporting documents from word or google docs can result in a markdown file that needs substantial cleanup.
Where do I find more information about markdown and plagiarism
https://laszewski.github.io/publication/las-20-book-markdown/
[https://cloudmesh-community.github.io/pub/vonLaszewski-writing.pdf]{.ul}
Can I use an online markdown editor?
There are many online markdown editors available. One of them is
[https://dillinger.io/]{.ul}.
Use them to write your document or check the one you have
developed in another editor such as word or google docs.
Remember, online editors can be dangerous in case you lose network connection. So we recommend to develop small portions and copy them into a locally managed document that you then check into github.com.
Github GUI (recommended): this works very well, but the markdown is slightly limited. We use hugo’s markdown.
pyCharm (recommended): works very well.
emacs (recommended): works very well
What level of expertise and effort do I need to write markdown?
What level of expertise is needed to learn BibTeX
How can I get IEEE formatted footnotes?
Will there be more FAQ’s?
Please see our book on markdown.
Discuss your issue in piazza; if it is an issue that is not yet covered, we will add it to the book.
How do I write URLs?
Answered in book
Note: All URL’s must be either in [TEXT](URLHERE) or <URLHERE> format.
This course introduces the students to Cloud Big Data Applications we provide the following sections
As part of this class, we will be using a variety of sources. To simplify the presentation we provide them in a variety of smaller packaged material including books, lecture notes, slides, presentations and code.
Note: We will regularly update the course material, so please always download the newest version. Some browsers try to be fancy and cache previous page visits. So please make sure to refresh the page.
We will use the following material:
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Course Lectures. These meeting notes are updated weekly (Web) |
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Introduction to AI-Driven Digital Transformation (Web) |
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A Gentle Introduction to Google Colab (Web) |
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A Gentle Introduction to Python on Google Colab (Web) |
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MNIST Classification on Google Colab (Web) |
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Piazza. The link for all those that participate in the IU class to its class Piazza. |
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Scientific Writing with Markdown (ePub) (PDF) |
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Git Pull Request. Here you will learn how to do a simple git pull request either via the GitHub GUI or the git command line tools |
This course does not require you to do much Linux. However, if you do need it, we recommend the following as starting point listed
The most elementary Linux features can be learned in 12 hours. This includes bash, editor, directory structure, managing files. Under Windows, we recommend using gitbash, a terminal with all the commands built-in that you would need for elementary work.
|
|
Introduction to Linux (ePub) (PDF) |
Older versions of the material are available at
|
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Lecture Notes 2020 (ePub) (PDF) |
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Big Data Applications (Nov. 2019) (ePub) (PDF) |
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Big Data Applications (2018) (ePub) (PDF) |
You can contribute to the material with useful links and sections that you find. Just make sure that you do not plagiarize when making contributions. Please review our guide on plagiarism.
This course does not require a sophisticated computer. Most of the things can be done remotely. Even a Raspberry Pi with 4 or 8GB could be used as a terminal to log into remote computers. This will cost you between $50 - $100 dependent on which version and equipment. However, we will not teach you how to use or set up a Pi or another computer in this class. This is for you to do and find out.
In case you need to buy a new computer for school, make sure the computer is upgradable to 16GB of main memory. We do no longer recommend using HDD’s but use SSDs. Buy the fast ones, as not every SSD is the same. Samsung is offering some under the EVO Pro branding. Get as much memory as you can effort. Also, make sure you back up your work regularly. Either in online storage such as Google, or an external drive.
This course introduces the REU students to various topics in Intelligent Systems Engineering. The course was taught in Summer 2020.
Selected chapters from out python Book
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Introduction to Python (ePub) (PDF) |
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Clinical Presentation |
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Management of COVID-19 |
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Investigational Therapeutics and Vaccine Development |
Pull from Computational Biology Journey
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SARS by the numbers |
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Principals |
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Summary |
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Introduction |
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Where Are We Now? |
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Where Will We Be Next? |
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Approaches to Long-Term Planning |
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1918-influenza-pandemic |
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2009 H1N1 pandemic |
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Soutch Korea 2020 |
Effect of AI on Industry and its Transformation Introduction to AI First Engineering
Examples of Applications of Deep Learning
Optimization – a key goal of Statistics, AI and Deep Learning
Learn the Deep Learning important words/components
Deep Learning and Imaging: It’s first greast success
For the BIg Data Class we revised the following material Big Data Overview Fall 2019
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20 Videos covering Big Data, technology, clouds and selected applications |
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18 Videos covering cloud computing |
The document is available as an online book in ePub and PDF
For ePub, we recommend using iBooks on macOS and calibre on all other systems.
The document is available as an online book in ePub and PDF from the following Web Page:
For ePub, we recommend using iBooks on Macos and calibre on all other systems.
THe book has over 590 pages. Topics coverd include:
Need to add material here
For questions on software, please mail Fugang Wang
Introduction to Machine Learning Using TensorFlow (pptx)
Introduction to using Colab from IU class E534 with videos and note (google docs) This unit includes 3 videos
Deep Learning for MNIST The docs are located alongside the video at
This teaches how to do deep learning on a handwriting example from NIST which is used in many textbooks
In the latter part of the document, a homework description is given. That can be ignored!
There are 5 videos
Jupyter notebook on Google Colab for COVID-19 data analysis ipynb
Follow-up on Discussion of AI remaking Industry worldwide
Class on AI First Engineering with 35 videos describing technologies and particular industries Commerce, Mobility, Banking, Health, Space, Energy in detail (youtube playlist)
Introductory Video (one of 35) discussing the Transformation - Industries invented and remade through AI (youtube)
Some online videos on deep learning
Optimization -- a key in Statistics, AI and Deep Learning (youtube)
Learn the Deep Learning important words and parts (youtube)
Deep Learning and Imaging: It's first great success (youtube)
Medical Student COVID-19 Curriculum - COVID-19 Curriculum Module 1 and then module 2
Multiscale, Virtual-Tissue Spatio-Temporal Simulations of COVID-19 Infection, Viral Spread and Immune Response and Treatment Regimes** VTcovid19Symp
This also needs Colab and deep learning background
A cursory and easy to understand review of climate issues in terms of AI: Tackling Climate Change with Machine Learning
For application in extreme weather event prediction, an area where traditional modelling methods have always struggled:
AI4ESS Summer School: Anyone who is interested in using Machine learning for climate science research I highly recommend you register for the Artificial Intelligence for Earth System Science summer school & interactive workshops which conveniently runs June 22^nd^ to 26^th^. Prior Experience with tensorflow/keras via google co-lab should be all the introductory skill needed to follow along. Register ASAP. https://www2.cisl.ucar.edu/events/summer-school/ai4ess/2020/artificial-intelligence-earth-system-science-ai4ess-summer-school
Kaggle Climate Change Climate Change Forecast - SARIMA Model with classic time series methods
Plotting satellite data Notebook (ipynb)
Accessing UCAR data (docx)
Hydrology with RNN and LSTM’s (more than 20 PDF’s)
Y. M. Bar-On, A. I. Flamholz, R. Phillips, and R. Milo, “SARS-CoV-2 (COVID-19) by the numbers,” arXiv [q-bio.OT], 28-Mar-2020. http://arxiv.org/abs/2003.12886 ↩︎
Jiangzhuo Chen, Simon Levin, Stephen Eubank, Henning Mortveit, Srinivasan Venkatramanan, Anil Vullikanti, and Madhav Marathe, “Networked Epidemiology for COVID-19,” Siam News, vol. 53, no. 05, Jun. 2020. https://sinews.siam.org/Details-Page/networked-epidemiology-for-covid-19 ↩︎
A. Adiga, L. Wang, A. Sadilek, A. Tendulkar, S. Venkatramanan, A. Vullikanti, G. Aggarwal, A. Talekar, X. Ben, J. Chen, B. Lewis, S. Swarup, M. Tambe, and M. Marathe, “Interplay of global multi-scale human mobility, social distancing, government interventions, and COVID-19 dynamics,” medRxiv - Public and Global Health, 07-Jun-2020. http://dx.doi.org/10.1101/2020.06.05.20123760 ↩︎
D. Machi, P. Bhattacharya, S. Hoops, J. Chen, H. Mortveit, S. Venkatramanan, B. Lewis, M. Wilson, A. Fadikar, T. Maiden, C. L. Barrett, and M. V. Marathe, “Scalable Epidemiological Workflows to Support COVID-19 Planning and Response,” May 2020. ↩︎
Luca Magri and Nguyen Anh Khoa Doan, “First-principles Machine Learning for COVID-19 Modeling,” Siam News, vol. 53, no. 5, Jun. 2020. https://sinews.siam.org/Details-Page/first-principles-machine-learning-for-covid-19-modeling ↩︎
[Robert Marsland and Pankaj Mehta, “Data-driven modeling reveals a universal dynamic underlying the COVID-19 pandemic under social distancing,” arXiv [q-bio.PE], 21-Apr-2020. http://arxiv.org/abs/2004.10666 ↩︎
Geoffrey Fox, “Deep Learning Based Time Evolution.”. http://dsc.soic.indiana.edu/publications/Summary-DeepLearningBasedTimeEvolution.pdf. ↩︎
T. J. Sego, J. O. Aponte-Serrano, J. F. Gianlupi, S. Heaps, K. Breithaupt, L. Brusch, J. M. Osborne, E. M. Quardokus, and J. A. Glazier, “A Modular Framework for Multiscale Spatial Modeling of Viral Infection and Immune Response in Epithelial Tissue,” BioRxiv, 2020. https://www.biorxiv.org/content/10.1101/2020.04.27.064139v2.abstract ↩︎
Yafei Wang, Gary An, Andrew Becker, Chase Cockrell, Nicholson Collier, Morgan Craig, Courtney L. Davis, James Faeder, Ashlee N. Ford Versypt, Juliano F. Gianlupi, James A. Glazier, Randy Heiland, Thomas Hillen, Mohammad Aminul Islam, Adrianne Jenner, Bing Liu, Penelope A Morel, Aarthi Narayanan, Jonathan Ozik, Padmini Rangamani, Jason Edward Shoemaker, Amber M. Smith, Paul Macklin, “Rapid community-driven development of a SARS-CoV-2 tissue simulator,” BioRxiv, 2020. https://www.biorxiv.org/content/10.1101/2020.04.02.019075v2.abstract ↩︎
Gagne II, D. J., S. E. Haupt, D. W. Nychka, and G. Thompson, 2019: Interpretable Deep Learning for Spatial Analysis of Severe Hailstorms. Mon. Wea. Rev., 147, 2827–2845, https://doi.org/10.1175/MWR-D-18-0316.1 ↩︎
Linux will be used on many computers to develop and interact with cloud services. Especially popular are the command line tools that even exist on Windows. Thus we can have a uniform environment on all platforms using the bash shell.
For ePub, we recommend using iBooks on MacOS and calibre on all other systems.
Topics covered include:
An important part of any scientific research is to communicate and document it. Previously we used LaTeX in this class to provide the ability to contribute professional-looking documents. However, here we will describe how you can use markdown to create scientific documents. We use markdown also on the Web page.
Scientific Writing with Markdown
The document is available as an online book in ePub and PDF
For ePub, we recommend using iBooks on macOS and calibre on all other systems.
Topics covered include:
OpenStack is usable via command line tools and REST APIs. YOu will be able to experiment with it on Chameleon Cloud.
We have put together from the chameleon cloud manual a subset of information that is useful for using OpenStack. This focusses mostly on Virtual machine provisioning. The reason we put our own documentation here is to promote more secure utilization of Chameleon Cloud.
Additional material on how to uniformly access OpenStack via a multicloud command line tool is available at:
We highly recommend you use the multicloud environment as it will allow you also to access AWS, Azure, Google, and other clouds from the same command line interface.
The Chameleon Cloud document is availanle as online book in ePub and PDF from the following Web Page:
The book is available in ePub and PDF.
For ePub, we recommend using iBooks on MacOS and calibre on all other systems.
Topics covered include:
Python is an easy to learn programming language. It has efficient high-level data structures and a simple but effective approach to object-oriented programming. Python’s simple syntax and dynamic typing, together with its interpreted nature, make it an ideal language for scripting and rapid application development in many areas on most platforms.
This online book will provide you with enough information to conduct your programming for the cloud in python. Although this the introduction was first developed for Cloud Computing related classes, it is a general introduction suitable for other classes.
The document is available as an online book in ePub and PDF
For ePub, we recommend using iBooks on macOS and calibre on all other systems.
Topics covered include:
We discuss in this module how to create a simple IPython Notebook to solve an image classification problem. MNIST contains a set of pictures.
This module will introduce you to how to use Google Colab to run deep learning models.
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In this module, we will take a look at some fundamental Python Concepts needed for day-to-day coding.
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In this module, we discuss how to create a simple IPython Notebook to solve an image classification problem. MNIST contains a set of pictures
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