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Courses

List of courses.

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

1 - 2021 REU Course

This course introduces the REU students to various topics in Intelligent Systems Engineering. The course was taught in Summer 2021.

Rstudio with Git and GitHub Slides

Rstudio with Git and GitHub Slides Rstudio with Git and GitHub Slides

Programming with Python

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.

Python Introduction to Python (ePub) (PDF)

Installation of Python

Installation of Python 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:

  1. Download Python version 3.9.5 just as shown in the first lecture.

  2. 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

  1. In case you use VSCode, you can also do it individually in a directory where you have your code.

    • On Mac: cd TO YOUR DIR; python3.9 -m venv .
    • On Windows 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.

  2. On Pycharm we recommend you use the ENV3 and set the clobal interpreter

Jupyter Notebooks

Jupyter Notebooks Jupyter Notebooks — June 7th, 2021 (PM): This lecture provides an introduction to Jupyter Notebooks using Visual Studio as IDE.

Github

Github Video: Github
Github 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.

Introduction to Python

Introduction to Python Slides: This introduction to Python cover the different data type, how to convert type of variable, understand and create flow control usign conditional statements.
Rstudio with Git and GitHub Slides Video-Introduction to Python (1) — June 9th, 2021 (AM): This introduction to Python cover the different data type, how to convert type of variable, understand and create flow control usign conditional statements.
Introduction to Python Video-Introduction to Python (2) — June 9th, 2021 (PM): This introduction to Python cover the different data type, how to convert type of variable, understand and create flow control usign conditional statements.
Introduction to Python Video-Introduction to Python (3) — June 10th, 2021 (AM): This lecture introduces the use of Google Colab to code your python program using the resources provided by Google. Also, DataFrame is introduced and use to manipulate and analyze data.
String, Numbers, Booleans Flow of control Using If statements Slides — June 10th, 2021 (PM): String, Numbers, Booleans Flow of control Using If statements
Slides: String, Numbers, Booleans Flow of control Using If statements Slides: String, Numbers, Booleans Flow of control Using If statements (2)
 Python Exercises Lab2 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.

  1. 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

    1. If the number is less than Zero return with an error message.
    2. Check to see if the number is Zero—if it is then the answer is 1—print this out.
    3. Otherwise use a loop to generate the result and print it out.
  2. 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

    1. If the user inputs a number below 2, print an error message.
    2. For any number greater than 2 loop for each integer from 2 to that number and determine if it can be divided by another number (you will probably need two for loops for this; one nested inside the other).
    3. For each number that cannot be divided by any other number (that is its a prime number) print it out.

Motivation for the REU

Video: Motivation for the REU: Data is Driven Everything Video — June 11th, 2021 (AM): Motivation for the REU: Data is Driven Everything
Slides: Motivation for the REU: Data is Driven Everything Slides: Motivation for the REU: Data is Driven Everything
Slides: Descriptive Statistic Slides: Descriptive Statistic
Slides: Probability Slides: Probability
Video: Motivation for the REU: Data is Driven Everything Video — June 28th, 2021 (AM): Working on GitHUb Template and Mendeley references management

Data Science Tools

Slides: Data Science Tools Slides: Data Science Tools
Numpy Video — June 14th, 2021 (AM): Numpy
Pandas data frame Video — June 14th, 2021 (PM): Pandas data frame
Web Data mining Video — June 15th, 2021 (AM): Web data mining
Pandas IO Video — June 15th, 2021 (PM): Pandas IO
Pandas Video — June 16th, 2021 (AM): Pandas
Matrix Computation Video-Matrix computation — June 16th, 2021 (PM): Linear algebra is a main compornent in the field of Data Science. As a consequence, this lecture introduces the main matrix operations such as, addition, substraction, multiplication, and picewise multiplication.
Pycharm Installation and Virtual Environment setup Video: Pycharm Installation and Virtual Environment setup — June 18th, 2021 (AM)
Application of Matrix Operation using Images on Python Video: This lecture the student can learn the different applications of Matrix Operation using images on Python. — June 21st, 2021 (AM)
Data wrangling Video: Data wrangling and Descriptive Statistic Using Python — June 21st, 2021 (AM)
Data wrangling and Descriptive Statistic Using Python Video: Data wrangling and Descriptive Statistic Using Python — June 22nd, 2021 (PM)
FURY Visualization and Microsoft Lecture Video: FURY Visualization and Microsoft Lecture — June 25th, 2021 (PM)
Introduction to Probability Video: Instroduction to Probability — June 25th, 2021 (PM)
Digital Twins and Virtual Tissue ussing CompuCell3D Simulating Cancer Somatic Evolution in nanoHUB Video: Digital Twins and Virtual Tissue ussing CompuCell3D Simulating Cancer Somatic Evolution in nanoHUB — July 2nd, 2021 (AM)

AI First Engineering

AI FIrst Engineering: Learning material Video: AI First Engineering: Learning material — June 25th, 2021 (AM)
Adding content to your su21-reu repositorirs Video: Adding content to your su21-reu repositories — June 17th, 2021 (PM)
AI First Engineering: Slides: AI First Engineering

Datasets for Projects

Datasets for Projects: Data world and Kaggle Video: Datasets for Projects: Data world and Kaggle — June 29th, 2021 (AM)
Datasets for Projects: Data world and Kaggle Video: Datasets for Projects: Data world and Kaggle part 2 — June 29th, 2021 (PM)

Machine Learning Models

K-Means: Unsupervised model Video: K-Means: Unsupervised model — June 30th, 2021 (AM)
Support Vector Machine: Supervised model Video: Support Vector Machine: Supervised model — July 2nd, 2021 (PM)
Support Vector Machine: Supervised model Slides: Support Vector Machine Supervised model.
Neural Networks: Deep Learning Supervised model Video: Neural Networks: Deep Learning Supervised model — July 6th, 2021 (AM)
SVideo: Neural Networks: Deep learning Model Video: Neural Networks: Deep learning Model — July 6th, 2021 (AM)
Data Visualization: Visualizaton for Data Science Video: Data Visualization: Visualizaton for Data Science — July 7th, 2021 (AM)
Convulotional Neural Networks: Deep learning Model Video: Convulotional Neural Networks: Deep learning Model — July 8th, 2021 (AM)

Students Report Help

Student Report Help Video: Student Report Help with Introduction and Datasets — July 7th, 2021 (AM)
Student Report Help Video: Student Report Help with Introduction and Datasets — July 13th, 2021 (AM)

COVID-19

Covid-19 Video: Chemo-Preventive Effect of Vegetables and Fruits Consumption on the COVID-19 Pandemic — July 1st, 2021 (AM)

2 - AI-First Engineering Cybertraining

This course introduces the students to AI-First principles. The notes are prepared for the course taught in 2021.

This is an image

Class Material

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:

Course Lectures and Management

Course Lectures Course Lectures. These meeting notes are updated weekly (Web)

Overview

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.

Lectures on Particular Topics

Introduction to AI-Driven Digital Transformation

Introduction to AI-Driven Digital Transformation (Web) Introduction to AI-Driven Digital Transformation (Web)

Introduction to Google Colab

A Gentle Introduction to Google Colab (Web) A Gentle Introduction to Google Colab (Web)
A Gentle Introduction to Python on Google Colab (Web) A Gentle Introduction to Python on Google Colab (Web)
MNIST Classification on Google Colab (Web) MNIST Classification on Google Colab (Web)
MNIST Classification with MLP on Google Colab (Web) MNIST-MLP Classification on Google Colab (Web)
MNIST Classification with RNN on Google Colab (Web) MNIST-RNN Classification on Google Colab (Web)
MNIST Classification with LSTM on Google Colab (Web) MNIST-LSTM Classification on Google Colab (Web)
MNIST Classification with Autoencoder on Google Colab (Web) MNIST-Autoencoder Classification on Google Colab (Web)
MNIST Classification with MLP + LSTM MNIST with MLP+LSTM Classification on Google Colab (Web)
Distributed Training with MNIST Distributed Training with MNIST Classification on Google Colab (Web)
PyTorch with MNIST PyTorch with MNIST Classification on Google Colab (Web)

Material

Health and Medicine

Sports Health and Medicine sector has become a much more needed service than ever. With the uprising of the Covid-19, resource usage, monitoring, research on anti-virals and many more challenging tasks were on the shoulders of scientists. To face such challenges, AI can become a worthy partner in solving some of the related problems efficiently and effectively.

AI in Banking

AI in Banking AI in banking has become a vital component in providing best services to the peopel. AI provides securing bank transactions, providing suggestions and many other services for the clients. And legacy banking systems are also being reinforced with novel AI techniques to migrate business models with technology.

Space and Energy

Space and Energy Energy is a term we find in everyday life. Conserving energy and smart usage is vital in managing energy demands. Here the role played by AI has become significant in recent years. Many efforts have been taken by industry leaders like Bill Gates to provide better solutions for efficient energy consumption. Apart from that Space explorations are also being reinforced with AI. Better communication, remote sensing, data analysis have become key components in succeeding the challenge to unravel the mysteries in the universe.

Mobility (Industry)

Mobility (Industry) Mobility is a key part in everyday life. From the personal car to space exploring rockets, there are many places that can be enhanced by using AI. Autonomous vehicles and sensing features provide safety and efficiency. Many motorcar companies have already moved towards AI to power the vehicles and provide new features for the drivers.

Cloud Computing

Cloud Computing Cloud computing is a major component of Today's service infrastructures. Artificial intelligence, micro-services, storage, virtualization and parallel computing are some of the key aspects of cloud computing.

Commerce

Commerce Commerce is a field which is reinforced with AI and technologies to provide a better service to the clients. Amazon is one of the leading companies in e-commerce. The recommendation engines play a major role in e-commerce.

Complementary Material

  • When working with books, ePubs typically display better than PDF. For ePub, we recommend using iBooks on macOS and calibre on all other systems.

Piazza

Piazza Piazza. The link for all those that participate in the IU class to its class Piazza.

Scientific Writing with Markdown

Markdown Scientific Writing with Markdown (ePub) (PDF)

Git Pull Request

Git Pull Request 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

Introduction to Linux

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.

Linux Introduction to Linux (ePub) (PDF)

Older Course Material

Older versions of the material are available at

Lecture Notes 2020 Lecture Notes 2020 (ePub) (PDF)
Big Data Applications (Nov. 2019) Big Data Applications (Nov. 2019) (ePub) (PDF)
Big Data Applications (2018) Big Data Applications (2018) (ePub) (PDF)

Contributions

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.

Computer Needs

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.

2.1 - Project Guidelines

We present here the AI First Engineering project guidelines

We present here the project guidelines

All students of this class are doing a software project. (Some of our classes allow non software projects)

Details

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.

Format of Project

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.

Possible sources of datasets

Given next are links to collections of datasets that may be of use for homework assignments or projects.

FAQ

  • 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?

    • Please go ahead and do so but make sure you use open-source data, and all results can be shared with everyone. If that is not the case, please pick a different project.
  • 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?

    • We taught 10-year-old students to use markdown in less than 5 minutes.
  • What level of expertise is needed to learn BibTeX

    • We have taught BibTeX to inexperienced students while using jabref in less than an hour (but it is not required for this course). You can use footnotes while making sure that the footnotes follow the IEEE format.
  • How can I get IEEE formatted footnotes?

    • Simply use jabref and paste and copy the text it produces.
  • 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.

3 - Big Data 2020

This course introduces the students to Cloud Big Data Applications. The notes are prepared for the course taught in 2020.

This is an image

Class Material

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:

Course Lectures and Management

Course Lectures Course Lectures. These meeting notes are updated weekly (Web)

Lectures on Particular Topics

Introduction to AI-Driven Digital Transformation

Introduction to AI-Driven Digital Transformation (Web) Introduction to AI-Driven Digital Transformation (Web)

Big Data Usecases Survey

Big Data Usecases Survey This module covers 51 usecases of Big data that emerged from a NIST (National Institute for Standards and Technology) study of Big data. We cover the NIST Big Data Public Working Group (NBD-PWG) Process and summarizes the work of five subgroups: Definitions and Taxonomies Subgroup, Reference Architecture Subgroup, Security and Privacy Subgroup, Technology Roadmap Subgroup and the Requirements andUse Case Subgroup. 51 use cases collected in this process are briefly discussed with a classification of the source of parallelism and the high and low level computational structure. We describe the key features of this classification.

Introduction to Google Colab

A Gentle Introduction to Google Colab (Web) A Gentle Introduction to Google Colab (Web)
A Gentle Introduction to Python on Google Colab (Web) A Gentle Introduction to Python on Google Colab (Web)
MNIST Classification on Google Colab (Web) MNIST Classification on Google Colab (Web)

Material

Physics

Physics Big Data Applications and Analytics Discovery of Higgs Boson Part I (Unit 8) Section Units 9-11 Summary: This section starts by describing the LHC accelerator at CERN and evidence found by the experiments suggesting existence of a Higgs Boson. The huge number of authors on a paper, remarks on histograms and Feynman diagrams is followed by an accelerator picture gallery. The next unit is devoted to Python experiments looking at histograms of Higgs Boson production with various forms of shape of signal and various background and with various event totals. Then random variables and some simple principles of statistics are introduced with explanation as to why they are relevant to Physics counting experiments. The unit introduces Gaussian (normal) distributions and explains why they seen so often in natural phenomena. Several Python illustrations are given. Random Numbers with their Generators and Seeds lead to a discussion of Binomial and Poisson Distribution. Monte-Carlo and accept-reject methods. The Central Limit Theorem concludes discussion.

Sports

Sports Sports sees significant growth in analytics with pervasive statistics shifting to more sophisticated measures. We start with baseball as game is built around segments dominated by individuals where detailed (video/image) achievement measures including PITCHf/x and FIELDf/x are moving field into big data arena. There are interesting relationships between the economics of sports and big data analytics. We look at Wearables and consumer sports/recreation. The importance of spatial visualization is discussed. We look at other Sports: Soccer, Olympics, NFL Football, Basketball, Tennis and Horse Racing.

Health and Medicine

Sports Health and Medicine sector has become a much more needed service than ever. With the uprising of the Covid-19, resource usage, monitoring, research on anti-virals and many more challenging tasks were on the shoulders of scientists. To face such challenges, AI can become a worthy partner in solving some of the related problems efficiently and effectively.

AI in Banking

AI in Banking AI in banking has become a vital component in providing best services to the peopel. AI provides securing bank transactions, providing suggestions and many other services for the clients. And legacy banking systems are also being reinforced with novel AI techniques to migrate business models with technology.

Transportation Systems

Transportation Systems Transportation systems is a vital component in human life. With the dawn of AI, transportation systems are also reinforced to provide better service for the people. Analyzing tera-bytes of data collected in day-to-day transportation activities are used to analyze issues and provide a better experience for the user.

Space and Energy

Space and Energy Energy is a term we find in everyday life. Conserving energy and smart usage is vital in managing energy demands. Here the role played by AI has become significant in recent years. Many efforts have been taken by industry leaders like Bill Gates to provide better solutions for efficient energy consumption. Apart from that Space explorations are also being reinforced with AI. Better communication, remote sensing, data analysis have become key components in succeeding the challenge to unravel the mysteries in the universe.

Mobility (Industry)

Mobility (Industry) Mobility is a key part in everyday life. From the personal car to space exploring rockets, there are many places that can be enhanced by using AI. Autonomous vehicles and sensing features provide safety and efficiency. Many motorcar companies have already moved towards AI to power the vehicles and provide new features for the drivers.

Cloud Computing

Cloud Computing Cloud computing is a major component of Today's service infrastructures. Artificial intelligence, micro-services, storage, virtualization and parallel computing are some of the key aspects of cloud computing.

Commerce

Commerce Commerce is a field which is reinforced with AI and technologies to provide a better service to the clients. Amazon is one of the leading companies in e-commerce. The recommendation engines play a major role in e-commerce.

Complementary Material

  • When working with books, ePubs typically display better than PDF. For ePub, we recommend using iBooks on macOS and calibre on all other systems.

Piazza

Piazza Piazza. The link for all those that participate in the IU class to its class Piazza.

Scientific Writing with Markdown

Markdown Scientific Writing with Markdown (ePub) (PDF)

Git Pull Request

Git Pull Request 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

Introduction to Linux

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.

Linux Introduction to Linux (ePub) (PDF)

Older Course Material

Older versions of the material are available at

Lecture Notes 2020 Lecture Notes 2020 (ePub) (PDF)
Big Data Applications (Nov. 2019) Big Data Applications (Nov. 2019) (ePub) (PDF)
Big Data Applications (2018) Big Data Applications (2018) (ePub) (PDF)

Contributions

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.

Computer Needs

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.

4 - REU 2020

This course introduces the REU students to various topics in Intelligent Systems Engineering. The course was taught in Summer 2020.

Computational Foundations

  • Brief Overview of the Praxis AI Platform and Overview of the Learning Paths
  • Accessing Praxis Cloud
  • Introduction To Linux and the Command Line
  • Jupyter Notebooks
  • A Brief Intro to Machine Learning in Google Colaboratory

Programming with Python

Selected chapters from out python Book

  • Analyzing Patient Data
  • Loops Lists Analyzing Data
  • Functions Errors Exceptions
  • Defensive Programming Debugging
Python Introduction to Python (ePub) (PDF)

Coronavirus Overview

Basic Virology and Immunology

Case Study: 1918 Influenza Pandemic Case Study: 1918 Influenza Pandemic: Prior to COVID-19, the 1918 influenza pandemic was the most severe pandemic in recent history. First identified in military personnel in the spring of 1918, the influenza was an H1N1 virus of avian origin. It is commonly referred to by scientists and historians as “the Mother of all Pandemics.” This pandemic is often referred to as the “Spanish Flu” in the lay press, though this name is a misnomer, and the virus likely originated elsewhere. Contemporary reporting focused heavily on Spain, as it was one of few places at the time that did not have restrictions on the press during World War I.
SnapShot: COVID-19 SnapShot: COVID-19: In December 2019, several cases of pneumonia of unknown origin were reported in Wuhan, China. The causative agent was characterized as a novel coronavirus, initially referred to as 2019-nCoV and renamed severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) (Zhou et al., 2020b). This respiratory illness, coronavirus disease 2019 (COVID-19), has spread rapidly by human-to-human transmission, caused major outbreaks worldwide, and resulted in considerable morbidity and mortality. On March 11, 2020, WHO classified COVID-19 as a pandemic. It has stressed health systems and the global economy, as governments balance prevention, clinical care, and socioeconomic challenges.
Basic Virology and Immunology Basic Virology and Immunology: In December 2019, a series of cases of pneumonia of unknown origin were reported in Wuhan, the capital city of Hubei province in China. The causative virus was isolated and characterized in January 2020 (Zhou et al., Nature 2020, Zhu et al., NEJM 2020). On January 12, 2020, the World Health Organization (WHO) tentatively named the virus as the 2019 novel coronavirus (2019-nCoV). On January 30, 2020 WHO issued a public health emergency of international concern (PHEIC) and on February 11, 2020, the WHO formally named the disease caused by the novel coronavirus as coronavirus disease 2019 (COVID-19). At that time, based on its genetic relatedness to known coronaviruses and established classification system, the International Committee on Taxonomy of Viruses classified and renamed 2019-nCoV as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). On March 11, 2020, the WHO formally characterized the global spread of COVID-19 as a pandemic, the first to be caused by a coronavirus.

Clinical Presentation

Clinical Presentation Clinical Presentation

Management of COVID-19

Management of COVID-19 Management of COVID-19

Investigational Therapeutics and Vaccine Development

Investigational Therapeutics and Vaccine Development Investigational Therapeutics and Vaccine Development

Coronavirus Genomics Superlab

Pull from Computational Biology Journey

SARS by the numbers

SARS by the numbers SARS by the numbers

Epidemiology

Introduction to Epidemiological Terms

Principals Principals
Summary Summary
Introduction Introduction

Where Are We Now?

Where Where Are We Now?

Where Will We Be Next?

Where Will We Be Next? Where Will We Be Next?

Approaches to Long-Term Planning

Approaches to Long-Term Planning Approaches to Long-Term Planning

Case Studies

Case Studies 1918-influenza-pandemic
2009 H1N1 pandemic
Soutch Korea 2020

Introduction to AI/Deep Learning

Deep Learning in Health and Medicine B: Diagnostics

Deep Learning in Health and Medicine C: Examples

Deep Learning in Health and Medicine D: Impact of Corona Virus Covid-19

Deep Learning in Health and Medicine E: Corona Virus Covid-19 and Recession

Deep Learning in Health and Medicine F: Tackling Corona Virus Covid-19

Deep Learning in Health and Medicine G: Data and Computational Science and The Corona Virus Covid-19

Deep Learning in Health and Medicine H: Screening Covid-19 Drug Candidates

Deep Learning in Health and Medicine I: Areas for Covid19 Study and Pandemics as Complex Systems

REU Projects

  • REU Individual Project Overview and Expectations
  • Accessing the Coronavirus Datasets
  • Primer on How to Analyze the Data

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

Big Data, technology, clouds and selected applications

Bigdara 20 Videos covering Big Data, technology, clouds and selected applications

Cloud Computing

Case Studies 18 Videos covering cloud computing

5 - Big Data 2019

This coursebook introduces the students to Cloud Big Data Applications

Big Data Applications

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.

6 - Cloud Computing

This is a large volume that introduces you to many aspects of cloud computing.

Cloud Computing

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:

  • DEFINITION OF CLOUD COMPUTING
  • CLOUD DATACENTER
  • CLOUD ARCHITECTURE
  • CLOUD REST
    • NIST
    • GRAPHQL
  • HYPERVISOR
    • Virtualization
      • Virtual Machine Management with QEMU
  • IAAS
    • Multipass
    • Vagrant
    • Amazon Web Services
    • Microsoft Azure
    • Google IaaS Cloud Services
    • OpenStack
    • Python Libcloud
    • AWS Boto
    • Cloudmesh
  • MAPREDUCE
    • HADOOP
    • SPARK
    • HADOOP ECOSYSTEM
    • TWISTER
    • HADOOP RDMA
  • CONTAINERS
    • DOCKER
    • KUBERNETES
    • Singularity
  • SERVERLESS
    • FaaS
    • Apache OpenWhisk
    • Kubeless
    • OpenFaaS ` * OpenLamda
  • MESSAGING
    • MQTT
    • Apache Avro
  • GO

7 - Data Science to Help Society

In this module, we will learn how to apply data science for the good of society. We introduce two examples, one for COVID-19, the other for hydrology.

COVID 101, Climate Change and their Technologies

General Material

Python Language

Need to add material here

Using Google CoLab and Jupyter notebooks

  • For questions on software, please mail Fugang Wang

    • Fugang can also give you help on python including introductory material if you need extra
  • 5 notebooks from Google

  • 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

    • How to create a colab notebook (mp4)

    • How to create a simple program (mp4)

    • How to do benchmark (mp4)

  • Deep Learning for MNIST The docs are located alongside the video at

    • Introduction to MNIST

    • 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

      1. DNN MNIST Introduction (mp4)

      2. DNN MNIST import section (mp4)

        • Running into import errors starting at the keras.models line in the code
      3. DNN MNIST data preprocessing (mp4)

      4. DNN MNIST model definition (mp4)

      5. DNN MNIST final presentation (mp4)

  • 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

    • Introduction to AI First Engineering (youtube)
  • Examples of Applications of Deep Learning (youtube)

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)

Covid Material

Covid Biology Starting point

Medical Student COVID-19 Curriculum - COVID-19 Curriculum Module 1 and then module 2

Compucell3D Modelling material

Interactive Two-Part Virtual Miniworkshop on Open-Source CompuCell3D

Multiscale, Virtual-Tissue Spatio-Temporal Simulations of COVID-19 Infection, Viral Spread and Immune Response and Treatment Regimes** VTcovid19Symp

  • Part I: Will be presented twice:
  • First Presentation June 11th, 2020, 2PM-5PM EST (6 PM- 9PM GMT)
  • Second Presentation June 12th, 9AM - 12 noon EST (1 PM - 4 PM GMT)
  • Part II: Will be presented twice:
  • First Presentation June 18th, 2020, 2PM-5PM EST (6 PM- 9PM GMT)
  • Second Presentation June 19th, 9AM - 12 noon EST (1 PM - 4 PM GMT)

Topics in Covid 101

  • Biology1 and Harvard medical school material above
  • Epidemiology2
  • Public Health: Social Distancing and Policies3
  • HPC4
  • Data Science 5,6,7
  • Modeling 8,9

Climate Change Material

Topics in Climate Change (Russell Hofmann)

References


  1. 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 ↩︎

  2. 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 ↩︎

  3. 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 ↩︎

  4. 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. ↩︎

  5. 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 ↩︎

  6. [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 ↩︎

  7. Geoffrey Fox, “Deep Learning Based Time Evolution.”. http://dsc.soic.indiana.edu/publications/Summary-DeepLearningBasedTimeEvolution.pdf↩︎

  8. 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 ↩︎

  9. 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 ↩︎

  10. 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 ↩︎

8 - Intelligent Systems

This book introduces you to the concepts used to build Intelligent Systems.

Intelligent Systems Engineering

The book is available in ePub and PDF

9 - Linux

You will learn here about using Linux while focussing mostly on shell command line usage.

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:

  • Linux Shell
  • Perl one liners
  • Refcards
  • SSH
    • keygen
    • agents
    • port forwarding
  • Shell on Windows
  • ZSH

10 - Markdown

Show your user how to work through some end to end examples.

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:

  • Plagiarism
  • Writing Scientific Articles
  • Markdown (Pandoc format)
  • Markdown for presentations
  • Writing papers and reports with markdown
  • Emacs and markdown as an editor
  • Graphviz in markdown

11 - OpenStack

You will have the opportunity to learn more about OpenStack. OpenStack is a Cloud toolkit allowing you to do Bare metal and virtual machine provisioning. Show your user how to work through some end to end examples.

OpenStack is usable via command line tools and REST APIs. YOu will be able to experiment with it on Chameleon Cloud.

OpenStack with 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:

  • Using Chameleoncloud more securely
  • Resources
  • Hardware
  • Charging
  • Getting STarted
  • Virtual Machines
  • Commandline Interface
  • Horizon
  • Heat
  • Bare metal
  • FAQ

12 - Python

You will find here information about learning the Python Programming language and learn about its ecosystem.

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.

Introduction to Python

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.

Introduction to Python

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:

  • Python Installation
    • Using Multiple different Python Versions
  • First Steps
    • REPL
    • Editors
    • Google Colab
  • Python Language
  • Python Modules
  • Selected Libraries
    • Python Cloudmesh Common Library
    • Basic Matplotlib
    • Basic Numpy
    • Python Data Management
    • Python Data Formats
    • Python MongoDB
  • Parallelism in Python
  • Scipy
  • Scikitlearn
  • Elementary Machine Learning
  • Dask
  • Applications
    • Fingerprint Matching
    • Face Detection

13 - MNIST Classification on Google Colab

In this mini-course, you will learn how to use Google Colab while using the well known MNIST example

MNIST Character Recognition

We discuss in this module how to create a simple IPython Notebook to solve an image classification problem. MNIST contains a set of pictures.

Prerequisite

  • Knowledge of Python
  • Google account

Effort

  • 1 hour

Topics covered

  • Using Google Colab
  • Running an AI application on Google Colab

1. Introduction to Google Colab

This module will introduce you to how to use Google Colab to run deep learning models.

A Gentle Introduction to Google Colab (Web)

2. (Optional) Basic Python in Google Colab

In this module, we will take a look at some fundamental Python Concepts needed for day-to-day coding.

A Gentle Introduction to Python on Google Colab (Web)

3. MNIST On Google colab

In this module, we discuss how to create a simple IPython Notebook to solve an image classification problem. MNIST contains a set of pictures

MNIST Classification on Google Colab (Web)

Assignments

  1. Get an account on Google if you do not have one.
  2. Do the optional Basic Python Colab lab module
  3. Do the MNIST Colab module.

References