AI/ML links

Book Synthesis Examples
• Book Synthesis Sample A
• Book Synthesis Sample B

Book List more-or-less related to AI/Machine Learning

• Burkov, Andriy. The Hundred-Page Machine Learning Book (technical book for beginners)
• Domingos, Pedro. The Master Algorithm (the five core algorithms in AI/ML. Non-technical)
• Kurzweil, Ray. The Singularity is Near (best intro to thinking clearly about the future)
• Kurzweil, Ray. How to Create a Mind (neuroscience and technology create intelligence)
• McGrayne, Sharon B. The Theory That Would Not Die (history of Bayesian Statistics)
• Mlodinow, Leonard. The Drunkard’s Walk: How randomness rules our lives (Great!)
• Page, Scott. The Model Thinker (by top professor of simulation (also known as modeling)
• Pinker, Stephen. Words & Rules (non-technical analysis of human brain algorithms)
• Schneier, Bruce. Data & Goliath (non-technical analysis of value of data vs privacy)
• Stephenson, Neal. The Diamond Age: or a young girl’s illustrated primer (education & AI)
• programming books on R or Python or AI library like Tensorflow

Sources of Data

• R Datasets (preinstalled with normal installation of R). Each one includes description of variables and sample R code: https://stat.ethz.ch/R-manual/R-patched/library/datasets/html/00Index.html
• Google Dataset Search: https://toolbox.google.com/datasetsearch
  New (Jan, 2020) Google Dataset Search (25 million datasets): https://datasetsearch.research.google.com/
• NOAA (the US National Oceanic and Atmospheric Administration) operates weather stations that measure surface temperatures at different sites around the United States. The hourly readings are publicly available: http://www.ncdc.noaa.gov/qclcd/QCLCD?prior=N
• U.S. Census Data:  https://www2.census.gov/programs-surveys
• MovieLens / GroupLens movie recommender system. Research project with choice of recommender algorithms and links to open datasets (Google search listing): https://www.google.com/search?client=safari&rls=en&q=movielens&ie=UTF-8&oe=UTF-8
• Boston Housing Dataset: http://www.cs.toronto.edu/%7Edelve/data/boston/bostonDetail.html
• Kaggle.com –The Home of Data Science: http://kaggle.com maintains over 14,000 TAGGED datasets useful to bring into an R or Python environment to explore. A free account is required, then you can access the datasets at: https://www.kaggle.com/datasets
• 19 Free Data Sets (many are highly useful and current: https://www.springboard.com/blog/free-public-data-sets-data-science-project/
• Gapminder Data Sets (global datasets, not always current, but awesome. Especially for social sciences). https://gapminder.org/data

K-12 Projects

• Overview of potential AI applications in Education: https://hechingerreport.org/ai-in-education-reframing-ed-tech-to-save-teachers-time-and-reduce-workloads/
• Chatbots (AI application, not AI algorithms): 3-video tutorial series gets teacher and student started with Snatchbot, an easy-to-learn but powerful system that’s free for simple projects with https://Snatchbot.me
  – 7:30-min beginner :  https://www.youtube.com/watch?v=YnNwCobbUdA
  – 7-min add NLP AI:  https://www.youtube.com/watch?v=Fz7_0IxfJBw
  – 4-min put chatbot on website: https://www.youtube.com/watch?v=1swBBSCrMkA
• Many “pretrained” NLP models to use right away!!!
• “Intent” and “Entity” drive NLP models. Intent is what you want to get from chatbot (MEANING). Entity is what chatbot searches for re: specific items searched for in discussion (specific WORDS that may connect to MEANING)
• true story of 8-12 year olds programming chatbot for Facebook: https://chatbotsmagazine.com/small-bot-the-kids-and-the-future-e82374aadc00

Texts for In-Depth AI/ML Study

• Maini, V, Sabri, S. (2017). Machine Learning for Humans. (free pdf download). https://medium.com/machine-learning-for-humans/why-machine-learning-matters-6164faf1df12
• James, G., Witten, D., Hastie, T., Tibshirani, R. (2013). An Introduction to Statistical Learning: with applications in R. (8th printing, 2017). http://www-bcf.usc.edu/%7Egareth/ISL/ISLR%20Seventh%20Printing.pdf
• website for above Intro. to Statistical Learning book with datasets and R package: http://www-bcf.usc.edu/~gareth/ISL/
• Raschka, S. & Mirjalili, V. (2017). Python Machine Learning (2nd ed). ($5 ebook in ePub, mobi, pdf … free code examples): https://www.packtpub.com/big-data-and-business-intelligence/python-machine-learning-second-edition
• Michael Nielson’s free online book, “Neural Networks and Deep Learning:” http://neuralnetworksanddeeplearning.com

Texts & Resources Specific to Statistics and R

• James, G., Witten, D., Hastie, T., Tibshirani, R. (2013). An Introduction to Statistical Learning: with applications in R. (8th printing, 2017). http://www-bcf.usc.edu/%7Egareth/ISL/ISLR%20Seventh%20Printing.pdf
• website for above Intro. to Statistical Learning book with datasets and R package: http://www-bcf.usc.edu/~gareth/ISL/
• Maindonald, J. H. (2008). Using R for Data Analysis and Graphics: Introduction, Code, and Commentary. Outstanding reference for R in traditional social science statistics. https://cran.r-project.org/doc/contrib/usingR.pdf
• Brief introduction to R from Duke University. Just the raw basics, especially helpful if you know other programming languages.: https://bigdata.duke.edu/sites/bigdata.duke.edu/files/site-images/An%20Introduction%20to%20Data%20Analysis%20with%20R_Fall%202017.pdf

Models of Machine Learning (5 models per Pedro Domingos)

• 40-min 2019 talk. Pedro Domingos, Demystifying Artificial Intelligence & Maching Learning: What you need to know 2019 talk at New York University Stern School of Business:  https://www.youtube.com/watch?v=hlEzVgKzZ3w. BEST overall conceptual explanation by a real expert in the field. Long intro(10-min), then 40 solid minutes, then 30 min Q&A. Covers 5 tribes of ML, the three problems that all MS algorithms must solve, and how we may get to a master algorithm that can learn anything well.
• 20-min 2016 TED talk on ML importance and simple explanations of 5 models and search for the Master Algorithm: https://www.youtube.com/watch?v=qIZ5PXLVZfo
• 12-min 2017 TED talk on the Next 100 Years of Your Life — a responsible ML researcher projects the coming changes to YOUR life!: https://www.youtube.com/watch?v=r2YiRiLAU_Y

Machine Learning with Python (and Scikit-Learn)

• Video and Jupyter Notebook series “Machine Learning w Scikit-Learn.” Playlist: https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A
• NOTE: videos are outstanding but several years old during which Python and its libraries have evolved. Up-to-date Jupyter Notebook video scripts are in Github: https://github.com/justmarkham/scikit-learn-videos
• Install Python, Jupyter Notebook, SciKit-Learn, numpy, and appropriate libraries with the Anaconda Distribution and installation (select operating system Windows or MacOS … takes 5 GB disk space: https://docs.anaconda.com/anaconda/install/

Bayesian Statistics – Probabilistic Modeling

• The two views of probability used in statistics today (excellent SHORT introductory overview): https://www.dummies.com/education/science/biology/two-views-of-probability/
• Bayes’ Theorem: explained like you’re five: https://www.youtube.com/watch?v=2Df1sDAyRvQ
• Applying Bayes’ Rule to classifying the likelihood of real-world events, this illustrated example helps to visually understand the powerful Naïve Bayes Classifier algorithm. No coding necessary, the graphical example is broken into relatively simple math to arrive at probable outcomes. Great to join the concept to simple math before using R or Python. Watch Naïve Bayes Classifier (12-min): https://www.youtube.com/watch?v=CPqOCI0ahss
• A real-world example of the likelihood that a positive mammography test means you actually have cancer. There’s a bit of math and some symbols, but readers felt it provided a powerful explanation of Bayesian probabilistic modeling. DON’T be put off by the little bit of math and symbols. It really is a strong example worth the effort to think through: https://betterexplained.com/articles/an-intuitive-and-short-explanation-of-bayes-theorem/
• Zero-Math introduction to Markov Chain Monte Carlo (MCMC) models: https://towardsdatascience.com/a-zero-math-introduction-to-markov-chain-monte-carlo-methods-dcba889e0c50
• Probability Concepts explained: https://towardsdatascience.com/probability-concepts-explained-bayesian-inference-for-parameter-estimation-90e8930e5348
• Bayesian Modeling in R: https://towardsdatascience.com/linear-and-bayesian-modelling-in-r-predicting-movie-popularity-6c8ef0a44184 (note: uses movies database available on CRAN: https://CRAN.R-project.org/package=ggplot2movies
• Top 10 applications of Bayesian Networks: https://data-flair.training/blogs/bayesian-network-applications/
• A formal paper on current applications of Bayesian modeling. Lengthy and complex, but with examples from real-world DNA and many other applications: https://www.snn.ru.nl/personal/wimw/preprintWiegerinckBurgersKappenNeural.pdf

General AI/ML Potential

• COMPREHENSIVE list of articles and mini-courses from overview to in-depth. One of the best one-stop-shop for all but the most technical information on AI and ML: https://fpf.org/artificial-intelligence-and-machine-learning-primer-resources/
• Short article to demystify AI and Machine Learning for normal people: https://www.geeksforgeeks.org/demystifying-machine-learning/
• AI Potential to Offer Personalized Learning (The Atlantic, 2017): https://www.theatlantic.com/sponsored/vmware-2017/personalized-education/1667/
• 10 TED Talks on AI (wonderful variety!): https://www.analyticsvidhya.com/blog/2018/09/best-ted-talks-artificial-intelligence-must-watch/
• Kevin Kelley’s How AI can bring on a second industrial revolution (14-min): https://www.ted.com/talks/kevin_kelly_how_ai_can_bring_on_a_second_industrial_revolution
• David Kelnar’s The fourth industrial revolution: A primer on Artificial Intelligence:  https://medium.com/mmc-writes/the-fourth-industrial-revolution-a-primer-on-artificial-intelligence-ai-ff5e7fffcae1
• What is the short-term potential of Al and ML? Google talk by Peter Diamandis (44-min) on the expected ways that AI-empowered technologies are and will be changing our lives in dramatic ways (like elimination of cancer and rapid growth of literacy. NOTE: he’s speaking to an educated, Google audience, so expect that some of his ideas may be unfamiliar or referenced in ways that are new to you (Google the mysteries): https://www.youtube.com/watch?v=HJpKxnZ2JeY
• The REST of YOUR LIFE! A 40-minute conversation with one of the brightest people on the planet, Yuval Noah Harari, author of three award-winning books on HUMANITY (that means YOU). His vision is infused with AI and machine learning such that he focuses on the EFFECTS, never on the technologies. If you fully understand his message, it can change you life … for the REST of your life. His website, alone, should be worth a Master’s degree: https://www.ynharari.com. Here’s his November, 2018 conversation: https://www.youtube.com/watch?v=x6tMLAjPVyo
• A 2018 talk to Canadian entrepreneurs by Peter Diamandis (1 hr) on exponential growth and abundance in our future (much of it powered by AI and Machine Learning). THIS talk demonstrates the power of AI and ML to dramatically change your future and even more, the future of our children: https://www.youtube.com/watch?v=LNHNk2uj0sk
• Juergen Schmidhuber is a rock star AI scientist who shares his experience and his clear vision that our generation is creating a new form of intelligence, vastly superior to our own. Whether you like or dislike his predictions, they are critically-important ideas to understand. Here’s his 16-min TEDx talk from scenic Lake Como (2017): https://www.youtube.com/watch?v=-Y7PLaxXUrs
• Google Assistant natural language interactionamong customer, Google Assistant, and restaurant. Note how “natural” the interaction appears!: https://www.youtube.com/watch?v=-qCanuYrR0g
• How robots can evolve on their own though genetic algorithms: https://futurism.com/ai-powered-robots-reproduce-evolve/https://futurism.com/ai-powered-robots-reproduce-evolve/

AI & ML Case Studies

• AI Teaching Assistant accepted as human by online students (SP, ’18)
• AI Robot co-delivered two West Point lectures in ethics/philosophy course—and answered questions! (Fall, 2018): https://futurism.com/the-byte/humanoid-robot-philosophy-course
• Fully-automated JD.com warehouse in Shanghai (3-min): https://www.youtube.com/watch?v=RFV8IkY52iY
• How does AI enter a new industry or market? How about a conservative market that is resistant to change (like education?)? This Forbes article highlights a 2019 example of AI entering the construction industry, an industry dominated by tradition: https://www.forbes.com/sites/amyfeldman/2019/01/14/artificial-intelligence-comes-to-the-construction-site-startup-pillar-technologies-flags-problems-before-disaster-hits/
• Tiny example of billionaire-founded restaurant chain using AI to select new restaurant location. Good example of a specific application generalizable to any organization needing to expand: https://www.forbes.com/sites/pamelaambler/2019/01/28/meal-and-a-manicure-inside-a-billionaires-boomingand-unconventionalrestaurant-empire/
• Alibaba announced new level of machine reading with comprehension better than most humans: https://tech.slashdot.org/story/19/07/10/169213/alibaba-claims-new-record-in-ai-language-understanding/

Human — Machine PARTNERSHIPS (& Augmented Intelligence)

• Shayam Sankar’s 12-min 2014 TED Talk on the symbiosis of human and AI: As we explore AI and ML in education, this concept becomes critically important. Here’s the link: https://www.ted.com/talks/shyam_sankar_the_rise_of_human_computer_cooperation
• Garry Kasparov’s 2017 TED Talk, Don’t Fear Intelligent Machines, Work With Them (15-min). Kasparov is the world champion chess grand master beaten by IBM’s Big Blue supercomputer. He brings a very personal message of value to all of us about human and machine are strongest as partners: https://www.ted.com/talks/garry_kasparov_don_t_fear_intelligent_machines_work_with_them

Machine Learning Visualized

• Can a physical robot learn to move and behave like a human baby learns? Good short article on a simple robot arm that was programmed to learn; like a human baby, it learned how to operate itself, it’s gripper, and even to hold a marker. Short article: https://futurism.com/new-robot-self-awareness
• Learning to Walk: MS Deep Mind Humanoid Walking: https://www.youtube.com/watch?v=gn4nRCC9TwQ

AI & ML in Education

• Belgium ready to implement a program to introduce AI learning software in Flanders schools. Site may be paywalled: https://marketbrief.edweek.org/marketplace-k-12/coming-soon-huge-test-coming-artificial-intelligences-role-classrooms
• . They will use software from Century Tech whose AI uses a rule-based, adaptive learning approach.
• Outstanding cutting-edge ideas from a variety of machine learning experts. “What are some applications of machine learning in education?”  – https://www.quora.com/What-are-some-applications-of-machine-learning-in-education
• More AI and ML applications in education TODAY by experienced Forbes data writer:  https://www.forbes.com/sites/bernardmarr/2018/07/25/how-is-ai-used-in-education-real-world-examples-of-today-and-a-peek-into-the-future/
• AI as a teaching partner rather than competitor with concrete examples developed by Penn State instructional technologists:  https://www.ecampusnews.com/2018/11/15/ai-can-humanize-teaching-if-we-let-it/?all
• Not just AI, but schools keep increasing their investment in technology systems and tools, and AI/ML is not only an ever-larger component of this investment. It’s part of their marketing strategy to appear to be “cutting edge:” https://www.edsurge.com/news/2019-01-15-us-edtech-investments-peak-again-with-1-45-billion-raised-in-2018
• Will AI systems ever replace teachers? Here’s an interview with Derek Li, CEO of Squirrel AI, a company with over 1600 learning centers in 300 Chinese cities: https://www.edsurge.com/news/2019-01-23-how-much-artificial-intelligence-should-there-be-in-the-classroom
• Florida International University is testing an AI as a tutor for an online business course: https://campustechnology.com/articles/2019/01/29/florida-school-tests-ai-as-virtual-tutor-for-online-course.aspx
• Japanese school using AI to reduce school bullying and suicide: https://futurism.com/the-byte/school-bullying-artificial-intelligence
• Google develops Bolo, an app to teach reading in Hindi to Indian children: https://www.cnn.com/2019/03/07/tech/google-bolo-india-reading-app/index.html

AI & ML in Healthcare and other Services

• Google & DeepMind are building AI assistants for doctors and nurses: https://futurism.com/google-health-deepmind-streams-app
• Radiologist’s Guide to Artificial Intelligence (10-part series): https://towardsdatascience.com/introduction-to-our-radiology-ai-series-51d0e54cd3b9
• How to Build a Human using ML to decipher individual genetic differences. Ricardo Sabatini (2016). 16-min: https://www.ted.com/talks/riccardo_sabatini_how_to_read_the_genome_and_build_a_human_being
• AI identified rare genetic conditions using facial scan algorithms: https://www.theverge.com/2019/1/15/18183779/facial-recognition-ai-algorithms-detect-rare-genetic-disorder-fdna
• A new machine learning program can predict how long ovarian cancer patients will live. https://eurekalert.org/pub_releases/2019-02/icl-ai021319.php

Training and Courses in AI & ML

• MIT 1-week intensive course Introduction to Deep Learning. Developed by grad students, all lectures and slides are available from 2018 and 2017. Here’s the master page for the 2018 course: http://introtodeeplearning.com/2018/
• Amazon opens it’s Machine Learning courses to developers:  https://aws.amazon.com/blogs/machine-learning/amazons-own-machine-learning-university-now-available-to-all-developers/
• 5 Free “Courses” and learning resources (Including Harvard!): https://www.analyticsindiamag.com/5-best-data-science-resources-that-beginners-can-download-for-free/

Genetic/Evolutionary Algorithms

• Karl Sims famous “Evolving Virtual Creatures with Genetic Algorithms” (1994!!!) : https://www.youtube.com/watch?v=bBt0imn77Zg
• Karl Sims paper from Connection Machines Corporation on design of genetic algorithms and 3-D modeling for examples shown in above video: http://karlsims.com/papers/alife94.pdf. His website (http://karlsims.com/) is an interesting collection of his work.
• Lee Graham’s 3D Virtual Creature Evolution
  – archived stellaralchemy page: http://www.stellaralchemy.com/lee/index.php
  – current wiki/website resources: https://3dvce.fandom.com/wiki/Main_Page
  – video tutorial series: https://www.youtube.com/playlist?list=PL2jwwD2YDmtB7GbsTPbyRpk-OE55t-0W4
• Unshackling Evolution: Evolving Soft Robots with Multiple Materials (3.5-min). Watch simple blocky shapes LEARN to walk (ok, move): https://www.youtube.com/watch?v=EXuR_soDnFo
• Rednuht’s Genetic Algorithm Walkers (HTML5 in-browser interactive simulation) of humanoid walking evolution with best-of-generation and reporting on fitness of 20 simultaneous individual. Configure mutation probability, amount, and noise: https://rednuht.org/genetic_walkers/

Neural Networks: Machine Learning through Back-propagation

What’s a neural network and how does it work? Series of YouTube videos from introductory to complex math by master videographer and explainer 3Blue1Brown. First video: https://www.youtube.com/watch?v=aircAruvnKk

Neural networks connected to brain may drive future prosthetics:  https://futurism.com/the-byte/human-brain-neural-network

Writing my first Machine Learning Game (1/4): https://www.youtube.com/watch?v=ZX2Hyu5WoFg. The 4-part series takes you through a CONCEPTUAL understanding of building a neural network to train an avatar in a 3D game (in Unity3d). No code, just how to think about it. (1.5 million views!)

Deep Learning – Reinforcement Learning

• DeepMind using reinforcement learning to “learn” an avatar to run, jump, avoid obstacles (2017): DeepMind Learns Parkour (3:25 min) – https://www.youtube.com/watch?v=faDKMMwOS2Q
• OpenAI uses Generative Adversarial Network (probably) to train “Sumo Wrestlers” (2-min): https://www.youtube.com/watch?v=2cjkKnAxCug

Can AI Algorithms Help Human Decisions? The Brain & AI

• Computational Neuroscientist Tom Griffiths’ 12-min TED talk gives three examples of human-usable algorithms learned from machines: https://www.ted.com/talks/tom_griffiths_3_ways_to_make_better_decisions_by_thinking_like_a_computer
• Karl Friston is a highly respected and high-level theoretician fascinated by using the human brain’s goals and methods to inform machine learning (and vice versa). He describes his Free Energy hypothesis using terms and concepts from both machine learning and neurology. Here are two 14-min talks from him. I recommend you listen to the first and, if you don’t like it or understand it a bit, then don’t bother with the second.:
  • Embodied Cognition, the idea that our brains are only half of the story of thinking; we need to consider that your brain is a part of your body. Really is an important idea: https://www.youtube.com/watch?v=HW0JnjgCO3o
    • link to the robot that walks without a brain or muscles: http://www.extremetech.com/extreme/101525-bluebiped-a-human-like-walking-robot-that-requires-no-power-source
  • The Free Energy Principle is a hypothesis that all life and all thinking can be defined by the exchange of energy and information between the organism and its environment. The boundary between the two is called the “Markov Blanket.” This model uses concepts that are familiar to the machine learning field because there appear to be huge similarities in the theories behind human and machine learning. Understand what you can (don’t expect to understand it all): https://www.youtube.com/watch?v=NIu_dJGyIQI

Social and Societal Issues in Automation, AI & Machine Learning

• Robots are coming to MGM casinos! What do employees think? Will their jobs really be protected? https://hardware.slashdot.org/story/19/03/08/1919232/mgm-considers-replacing-workers-with-robots-in-its-las-vegas-strip-properties
• How do people react to algorithmic decisions? What helps people accept AI decisions, and what makes them resent or question AI decisions? https://www.wired.com/story/book-excerpt-algorithm-transparency
• Nicholas A. Christakis, a physician and sociologist, is the Sterling Professor of Social and Natural Science at Yale and the author of Blueprint: The Evolutionary Origins of a Good Society. In this April, 2019, Atlantic article “How AI Will Rewire Us,” he discusses the personal and social changes coming when people work and live closely with robots: https://www.theatlantic.com/magazine/archive/2019/04/robots-human-relationships

Concerns About AI and Machine Learning

• Real-world example: Joi Ito on the Boston School Bus Route Problem: https://www.wired.com/story/joi-ito-ai-and-bus-routes/
• Another machine learning example of “biased” learning and recommendations that critics say are a “crisis” in science. Here’s the BBC article on an AAAS study: https://www.bbc.com/news/science-environment-47267081
• China training brightest students to design intelligent weapons: https://tech.slashdot.org/story/18/11/08/0033220/chinas-brightest-children-are-being-recruited-to-develop-ai-killer-bots
• Future jobs: will jobs disappear? will new ones blossom? Here’s a 15-min overview of that fast-approaching threat to the social fabric as well as the individual worker. A possible down-to-earth solution is proposed: https://www.youtube.com/watch?v=swB7Ivct8d8
• Most people in the world want two things: a stable job and the dignity that is associated with that job. Here’s an 11-min TED interview with Roy Bahat, an AI venture capitalist, who has explored this issue for years. One of the best discussions of this critical issue: https://www.ted.com/talks/roy_bahat_and_bryn_freedman_what_is_the_meaning_of_work
• AGI: Artificial GENERAL Intelligence. What is it? When will it come? How will we or can we respond or prepare? Robin Winsor’s 17-min TED Talk on Preparing for a future with Artificial Intelligence: https://www.youtube.com/watch?v=f7dhOHMX0js
• Very little about AI and ML, but a long and relevant discussion about the near future of humanity: a 1-and-1/2 hour conversation between Yuval Noah Harari and Natalie Portman. If your have strong philosophical, historical, artistic, psychological, or women’s issue interest, then watch it. If you lean more to the technological or scientific perspective, it may not be for you. (I loved it.): https://www.youtube.com/watch?v=87XFTJXH9sc
• When AI beats scientists at their own specialty. Read the personal account of a Harvard biologist who came to terms with the superiority of a new AI (DeepMind) over an important part of his field: https://www.voxhio.com/future-perfect/2019/2/15/18226493/deepmind-alphafold-artificial-intelligence-protein-folding . There is a message here for all of us.
• Example of AI gone very bad! Facebook and other social media sites use AI to identify highly-emotional content and direct those links to a population susceptible to that content. So crazy stories are sent to people susceptible to “conspiracy theories” with tragic results. Question: is this an intended or unintended consequence of their algorithm and ethics? Here’s the link: https://futurism.com/drugs-rape-massacres-ai-exposing-children-worst-of-humanity
• Wired article that clarifies and questions AI’s current effectiveness in diagnosing medical conditions: https://www.wired.com/story/the-life-threatening-consequences-of-overhyping-ai/

Hands-on Examples in R

• Decision Tree Classifier in R (Iris dataset). Great 20-minute introduction, but first R code line must be install.packages(“rpart.plot”) : https://www.youtube.com/watch?v=JFJIQ0_2ijg
• Video comparing Naïve Bayes and k-Nearest-Neighbor algorithms using Australian Health Data to categorize citizens’ wellbeing. The 17-min video includes links to data and to R-script (links below video). Video shows how to explore refining or tuning the algorithms to improve predictions. Great example of the power of a few lines of R code to perform complex operations! Use the R-Script and FOLLOW THE VIDEO narration in R Stats: Naive Bayes and k-NN:https://www.youtube.com/watch?v=MbBvtnpcx2c

Hands-on Tutorials with TensorFlow

• Fast TensorFlow 2.0 hands-on tutorial. This uses Google’s Colab interactive programming environment (compatible with Jupyter Notebook). 60,000 hand-drawn numerals train a TensorFlow neural network to categorize hand-drawn digits from 0 to 9. The model is then tested with 10,000 hand-drawn digits. Note that the first “pip” command must be preceded by “!” exclamation mark character to send the command to the terminal (Colab or Jupyter can’t handle it): https://www.youtube.com/watch?v=fQCKxzHvYnw
• Tools for above tutorial: Google Collaborate (addition to Google Drive and can be shared) and Tensorflow Tutorials: https://www.tensorflow.org/tutorials/
• Article announcing TensorFlow 2.0 and it’s importance in making AI/ML accessible to teachers, students, and the world: https://hackernoon.com/tensorflow-is-dead-long-live-tensorflow-49d3e975cf04?sk=37e6842c552284444f12c71b871d3640