explaining machine learning to a 5th grader

tool made up of more than one simple machine. Science 5th grade Lessons 82-91 14 Terms.,,,, This website uses cookies to improve service and provide tailored ads. Note: The content in the post is simplified to a larger extent so that it suits the audience. Keys: Machine Learning, AI, Statistic, Supervised learning, Unsupervised learning, Deep learning, Neural network, Data. It turns out that on three of India's previous five wins, it had rained just before the match. This is a tough task, I was in this precarious situation trying to explain to my younger son. However, it differs from the classifiers previously described because it’s a lazy learner. A strong learner has much higher accuracy. Next, is it showing any signs of movement, or it is just lying on the ground? So, given only the information that we have, and everything else being equal you'd want to back South Africa. This is important information if you plan to bet on — if it is raining you should back India; if it is not, you should back South Africa. ), I will explain you later”. Learning Objective After researching and reading about biomes, students will be able to identify and describe the six major biomes of … Now, how does this extra information affect where you should put your money? ... You didn't just explain big data to a 5th grader -- you explained it to a … Now, given these information, you want to predict whether your classmates will accept your invitation or not. Dropping the threshold means you're more willing to go to the movie. But, we can’t say the same thing for height and weight because if height increases, weight likely increases. Teach your students key science concepts such as the difference between a discovery and an invention with these whiteboard compatible lessons. corinetran PLUS. This is very time consuming task and takes lot of your time. When new unlabeled data is fed into it, the eager learner feeds that into the classification model. Classroom exercises 2 + 3 + Amazon ! On the other hand, an eager learner builds a classification model during training. The end result is we find the best learner. If yes, then we are becoming surer that it might be a snake, and so on. So, p(A|B) is what we want to find out. Remember last time when we went on a jungle safari and the incident where you made heck of noise â€œShit! However, it seems lately we have found a close enough solution to mimic the human brain functions, Artificial Neural Networks (ANN), which is a base for the Deep Learning as well. The human brain works according to deep learning, though you may not be aware of your own brain doing those intermediate steps before coming up with the result. As a starting point to the conversation I asked him, list down your decision making points, meaning there may be many situations when you had to make decisions but you may not have all the information. This is supervised learning, since each iteration trains the weaker learners with the labelled dataset. Do you have an example? For example, imagine you're building a shopping recommendation engine, and you discover that if an item is trending and a user has browsed the category of that item in the recent past multiple times, you can deduce that he/she is very likely to buy the trending item. Musings of a Chief Analytics Officer: So…, Musings of a Chief Analytics Officer: What…. complex machine. It is truly amazing that generation Z is learning about Big Data, while they are in the 5th grade. (Your dad is not in the city in the weekend hence you will have to rely on public transport). Does your best friend who also takes football coaching along with you, want to accompany you for the movie? As an online shopping business, if you have lots of items and lots of customers, how do you determine to showcase the best choices to the consumers according to their tastes and preferences? Published on May 18, ... (remember all those examples I had given you earlier about Machine Learning Algorithms! Explaining Machine Learning to a 5th Grader Published on February 24, 2016 February 24, 2016 • 180 Likes • 27 Comments. In other words, previously misclassified outcomes have a higher chance of showing up in the sample. Alright, you don’t like your social studies paper, you have not seriously taken notes in class and have a very minimal understanding about the topics but you have an exam tomorrow. The project is about explaining what machine learning models are doing . So for Pratik the recommendations will come out in a ranked order as Phone 2, Phone 1, Phone 3, Phone 5 and lastly Phone 4. How does k-means take care of the rest? Because they can work with unsupervised data, which constitutes the majority of data in the world, deep neural nets can become more accurate than traditional shallow ML algorithms that are unable to handle unsupervised data. Decision tree learning creates something similar to a flowchart, at each point in the flowchart is a question about the value of some attribute, and depending on those values, he or she gets classified. After a few weeks, I gathered a lot of courage to explain him what “Deep Learning” is all about! C4.5/C5.0 develops a classifier in the form of a flow chart (decision tree) with conditions at each branching node. What is missing in our work so far is the item-user matrix, once we get that done it will be easier to recommend other users based on the similarity of the profiles. James Kotecki. Note that the probability of India winning given that it is raining is not at all the same as the probability of its being raining when India wins. Each classmate can fall into 1 of 2 classes: will accept your invitation or won’t. IoT. And the most critical activity in the data preparation is feature extraction/feature engineering. The larger value of w1 indicates that the weather matters a lot to you, much more than whether your best friend joins you, or the nearness of football coaching ground to the movie hall. Let's take a step back and now instead of jumping into a conclusion, we give more time to think and analyse! In each hemisphere of our brain, humans have a primary visual cortex containing 140 million neurons, with tens of billions of connections between them. Think critically about the importance of the machines they encounter in life. soumendra mohanty Follow 5 builds a decision tree classification model during training. Sure, remember when I ask you about your friends you have this way of explaining who are the nerds, who are the sports guys, who are friendly, who are talkative and who are the troublesome guys! If a computer does this it is called machine learning. How will you prepare for your exam? A lazy learner doesn’t do much during the training process other than storing the training data. SVM builds a hyperplane classification model during training. p(A|B) is the posterior (usually read 'probability of A given B') is the probability of finding observation A, given that some piece of evidence B is present. Big Data Analytics. How do we do that? This is called convergence. k-means creates â€˜k’ groups from a set of objects so that the members of a group are more similar. AdaBoost builds an ensemble classification model during training. Is the movie hall near to your football coaching ground? Clustering using Euclidian distance ! One more terminology you will encounter when doing SVM, it is called margin. Machine learning as a service (MLaaS) is an array of services that provide machine learning tools as part of cloud computing services. We humans are astoundingly good at making sense of what our eyes see, but interestingly nearly all the grunt work is done in the background. When new unlabeled data comes in, kNN operates in 2 basic steps: Do you have an example? The information contains their hobby, kind of books they read, do they share their tiffin or not, are they friendly, in your last birthday did they come and did they bring nice gifts, etc. What does it do? Is this supervised or unsupervised? Boosting is an ensemble learning algorithm which takes multiple learning algorithms and combines them. But what the example illustrates is how a perceptron can weigh up different kinds of evidence in order to make decisions. In this way a perceptron in the second layer can make a decision at a more complex and more abstract level than the perceptrons in the first layer. It turns out that India and South Africa have played against each other on twelve previous occasions. The sensible thing to do is to first study for some time (optimize X) then predict what are possible questions for the exam (expectations for Y), then do this cycle again and again until you are satisfied that you understand the topics very well (optimal X) that you are prepared for questions that are very likely to come in the exam (based on expected Y). Below table illustrates their past encounters and outcomes. In those scenarios, a 2 dimensional view of your toys won’t work hence you elevate your observation space to 3 dimensions and minutely examine what other additional characteristics will help you determine the classes. Musings of a Chief Analytics Officer: So…, Musings of a Chief Analytics Officer: What…. ... and they are easy to construct. The High School Integrated Conceptual Science Program (ICSP) is a NGSS-aligned curriculum that utilizes the conceptual progressions model for bundling of the NGSS, High School Conceptual Model Course 1 and strategies from Ambitious Science Teaching (AST) to focus on teaching practices needed to engage students in science discourse and learning. Note that the additional information — that India won three times out of five on a rainy day — shifts its probability of winning this current match from 41.7%, to 75%. Using the co-ordinates of Maaza (8,2) we will calculate the distance between  ‘Maaza’ and all other items and update our table. There is a widespread belief that at a broad level you do classification or regression using Machine Learning algorithms, it is true to a large extent. More importantly, it means that now India is more likely to win than South Africa, even though South Africa has won more matches overall. Explaining Explanations: An Overview of Interpretability of Machine Learning Abstract: There has recently been a surge of work in explanatory artificial intelligence (XAI). SVM does this in an automated way, maps them into a higher dimension and then finds the hyperplane to separate the classes. Pratyush likes a long lasting battery and a decent display performance. The first is the size of your itemset. 22. This is supervised learning, since kNN is provided a labeled training dataset. At the end of the 10 rounds: We’re left with an ensemble of weighted learners trained and then repeatedly retrained on misclassified data from the previous rounds. The line represents the hyperplane. If it's moving, then it's a lot more likely to be a snake. And even more complex decisions can be made by the perceptron in the third layer. We’re going to train them in 10 rounds on a training dataset containing your soccer team’s data. The best learner is again weighted and incorporated into the ensemble, misclassified outcomes are weighted so they have a higher chance of being picked and we repeat the steps. I took those and matched it to the machine learning algorithms while explaining the core concept behind the problem solving. The work will be presented at the International Conference on Machine Learning June 10-15. South Africa, on the other hand, appears to be a better bet at 7/12, or 0.583 or 58.3%. Then the perceptron would decide that you should go to the movie whenever the weather was good or when both the movie hall is near football coaching ground and your best friend is willing to join you. Let’s start with 3 weak learners. What about the perceptrons in the second layer? This feature hierarchy and the filters model the significance in the data in an automated way without any intervention. You will jump for 3 or 4 times and the 5th time you will correct yourself and stand still. At the end of the lesson, students will be able to define simple machines, identify the six types of simple machines, and use the correct terminology to describe how each simple machine works, and explain how simple machines make work easier. In order to properly understand the function of each machine, students will need to recall their knowledge of force, motion and potential energy. Cool, but how exactly this works in a decision tree? They hopefully won’t all be closest to the same one, so they’ll form a cluster around their nearest centroid. Note: A weak learner classifies with accuracy barely above chance. Every kid in your class will be closest to one of these k centroids. Simple Machines 11 Terms. However, it had lost only once when it had rained prior to the match. Explain Machine Learning to a grandfather could be a difficult task, especially that the technology industry has grown fast in the least years. While it showcases the automated AI capabilies of IBM Watson Studio with AutoAI, the course does not explain Machine Learning or Data Science concepts. Interesting part is you tell k-means algorithm how many clusters you want. Introducing Inventions Mini-Lesson; More Mini-Lessons; Printables Chapter 6 "Simple Machines" 30 Terms. So the value of p(A|B) — the probability of India winning given that it is raining — is. My initial reaction was, “son, I myself don’t know much, this is a very new and rapidly evolving area related to Machine Learning (remember all those examples I had given you earlier about Machine Learning Algorithms! Include printable teaching worksheets. Now when you introduce the data about the new team you have got a higher chance of predicting the right outcome. This is because three of its previous five wins have been on rainy days. Intuition for algorithms that find patterns in data ! Do you want to see patterns for a 2-itemset, 3-itemset, etc.? There is a simple rule to calculate the output – you assign weights which are real numbers expressing the importance of the respective inputs to the output. Now, assume that Maaza has the parameters as (8,2). Archimedes Screw Exploration from High Hill Education is a simple project using a plastic bottle that showcases how this invention made hundreds of years ago was able to move material. Hybrid Model Survival 101 As the pandemic stretches on, many of us are hyper-aware of the long-term consequences to our educational system if we cannot find ways to get students back into buildings. The two parameters are “sweetness” and “fizziness”. Below is the summary of that fascinating and sometimes frustrating conversation. On the other hand, if you use only the new information about the weather, and neglect the previous counts of wins and losses, you would perhaps back India. The algorithm predicts the class given a set of features using probability. Sure, Let’s consider few ’drinking items’ which are rated on two parameters on a scale of 1 to 10. However, to do this would be to ignore a crucial piece of information — that overall India has won fewer matches than South Africa. This means 67% percent of time we can be sure that a person buying chips and eggs will also buy soft drinks. LinkedIn recommends the new browser from Microsoft. But perhaps you really loathe bad weather, and there's no way you'd go to the movie or football coaching if the weather is bad. This is a supervised learning, since a dataset is used to first teach the SVM about the classes. The sample of your dataset is now influenced by the more heavily misclassified weights. EM is useful in Catch-22 situations where it seems like you need to know A before you can calculate B and you need to know B before you can calculate A. The word deep comes about because of the layer arrangements and signal propagation through the structure from first/input layer to the deeper/output layer. Sure, remember sometime back you were asking me who you want to invite to your birthday party and whether they will accept your invitation or not! Pushing, pulling, and lifting are all common forms of work. Why would you do this? You might make your decision by weighing up three factors: We can represent these three factors by corresponding binary variables x1, x2, and x3. Yes, virtual teaching is improving with each passing week, but we all long to be in closer contact with students, particularly those who are struggling to receive basic needs. What is a hyperplane? If you do a “shallow learning”, you will pick the two important features – “item is trending” and “recency of the browsed item is high”, apply a ML technique (likes of Logistic Regression) and mark the user as “likely to buy = yes”. Cluster analysis forms groups such that the group members are more similar versus non-group members. Learn more: Rookie Parenting. If it's on the dirt path it may not be a snake, but if it is in the bush then most probably it is a snake. In this network, the first layer of perceptrons is making three very simple decisions by weighing the input evidence. Of these last twelve matches, India won five and South Africa won the other seven. If the toys are mixed together, a straight line won’t work. By optimizing the likelihood, EM generates a model that assigns class labels to data points (This looks like one more clustering technique!). Organize students into partners for research gathering and written work. AI/Machine Learning. What seems easy when we do it ourselves suddenly becomes extremely difficult. This course is intended for practicing Data Scientists. This algorithm discovers frequent sets of items (for example, items purchased together in a supermarket) and then finds out association rules based on these itemsets. Besides the curated list of top 10 Machine Learning algorithms we discussed above, there are hundreds of algorithms that have various modifications and implementation approaches. What does it do? Explain, or ask students to explain, ways to read and write this decimal (one-tenth, 0.1, or 1/10). Wait, what’s a classifier? In machine learning, artificial neural networks (ANNs) are a family of models inspired by biological neural networks (the central nervous systems of animals, in particular the brain) and are generally presented as systems of interconnected "neurons" which exchange messages between each other. By using this site, you agree to this use. A simple machine is a device used to assist people in getting work done faster and easier. So what is the probability of India winning, given that it is raining? That's the basic mathematical model. Then it follows an iterative 3-step process: This is very complicated, do you have an example? K-means takes care of the rest. What we need to do is to combine the two pieces of information to get some kind of overall probability of India winning the match. Similarly, x2 = 1 if your best friend wants to go with you to the movie, and x2 = 0 if not. Clusters and groups are synonymous in the world of cluster analysis. The second is your support or the number of transactions containing the itemset divided by the total number of transactions. Is this supervised or unsupervised? In round 1: AdaBoost takes a sample of the dataset and tests to see how accurate each learner is predicting the outcomes against the known teams. This center becomes the new centroid for the cluster. What does it do? What does it do? The activation function then decides whether to move forward and trigger inputs to the next layer or not. The margin is the distance between the hyperplane and the two closest data points from each respective class. The perceptron's output is 0 or 1 and is determined by whether the weighted sum is less than or greater than some threshold value. And similarly again for x3 = 1 if the movie hall is near to your football coaching ground and you and your best friend are ok to take a public transport, and x3 = 0 if not. Launch your own bottle rocket. For instance, we'd have x1 = 1 if the weather is good, and x1 = 0 if the weather is bad. We told it first, it generated a decision tree, and now it uses the decision tree to classify. Just like the weights, the threshold is a real number which is a parameter of the perceptron. The more layers or deeper the system is, the more it can handle complex data. About: Machine Learning and AI (artificial intelligence) course for kids and youth (teens) is a program that teaches the Machine Learning and AI (artificial intelligence) tools that can be used for building intelligent ma-chines. Give me an example. Young readers will learn how to describe fictional characters by identifying internal and external traits and providing concrete evidence to support their thinking. This is an intelligent process, called feed-forward, in which what you perceive is influenced by what you expect given the context. Now you see how a multi layered neural network is helping you make a decision! This process is called ensemble learning method. The depth of the neural net allows it to construct a feature hierarchy of increasing abstraction, with each subsequent layer acting as a filter for more and more complex features that combine those of the previous layer. What’s an example of AdaBoost? This is the probability of the evidence turning up, given that the outcome obtains. Is this supervised or unsupervised? What does it do? In our example, p(A) is 5 / 12, or 0.417, because India won on five out of twelve occasions. regina_galati. The dataset contains details about your past matches, goals scored, goals against, your key players and their performance, and also your opponent team’s performance, etc. Build a snack machine. It is not a snake but just a rope, move on". Just from a visual inspection, you triggered the thought about the shape and size and also in a subconscious way you got influenced to think about snakes because we were on a jungle safari. Is this supervised or unsupervised? Jun 22, 2020 - Explore VickieMarie's board "reading comprehension videos", followed by 365 people on Pinterest. Elementary Science Biomes, flora and fauna, habitats, adaptations . OTHER SETS BY THIS CREATOR. Enhance your teaching strategies and increase students' learning with these mini-lessons and slideshows. This is called deep learning. The main objective of this is to provide the most relevant and accurate items to the user by filtering useful stuff from of a huge pool of information base. CART stands for classification and regression trees. This simple idea lends itself beautifully to a demonstration that … Blast off with a few supplies and a little help from the laws of motion. Simple Machine Lesson Plans. Therefore, all other things being equal, the probability of India winning the next match can be estimated from previous wins: 5 / 12, or 0.417, or 41.7%. There are two things you need to keep in mind. What does it do? In case of a shallow learning, the feature engineering activity is mostly manual. Reserve computers or time in the computer lab as well. M-step: Updates the parameters based on the cluster assignments from the E-step. You can use perceptrons to model this kind of decision-making. In addition, the cases where the learner misclassified the outcome are given a heavier weight, so that they have a higher chance of being picked in the next round. We know that India achieved three of its five wins on rainy days, and it only rained once when it lost. Angelina 2nd Grade Science (Physics- Simple Machines) 12 Terms. Now, from the past purchases and user feedbacks you have got a dataset which you will use to develop a user-feature matrix as below. If you have got a massive dataset, you can use clustering technique to get an overall feel of what kind of distinct behaviors are there in the data and then carry on your analysis from there onwards. This can include tools for data visualization, facial recognition, natural language processing, image recognition, predictive analytics, and deep learning. Educational Standards As engineering the features is a time consuming task, you apply all your ML experience and domain knowledge to extract only the relevant features that improve your model. Deep learning refers to neural nets with more than one hidden layer. We get down to do a detailed inspection bringing in layers of thoughts, each layer providing inputs to the next layer and so on, till we come to a convincing conclusion that this is not a snake but a rope. There are basically two things one need to construct – Item-Feature Matrix and then User-Feature Matrix. The basic approach to learning is the same with machines as it is with animals, businesses, and people: Reinforce good choices, discourage bad ones (alternatively ignore them, depending on your parent style). The connections have numeric weights that can be tuned based on experience, making neural networks adaptive to inputs and capable of learning. In real world scenario, the features of a dataset are generally not all independent. What do the toys, table and line represent? Let's assume that there is a cricket match between two teams: India and South Africa, and you want to determine which team will win. The first place to the right of the decimal point is the tenths place. So you have a fifth grader?Whether you are a homeschooler, teacher, or are a parent supplementing your child's education -- we've got lots of fun, creative educational activities for you!This page is filled with over pages of 5th grade math worksheets, grade 5 math games, and activities to make learning Math, English / Language Arts, Science, Social Studies, Art, Bible, Music, and more FUN! We have already calculated this probability as 60% (three rainy days out of five wins). To install LIME, execute the following line from the Terminal: pip install lime. A hyperplane is a function which creates a distinct boundary separating the two classes. However, if you want to determine what ranges of %marks (30%-40%, 40%-60%, 60%-80%, etc) they will pass with then CART develops a regression tree. When you try to make such programs, you quickly get lost in a long list of scenarios, rules and exceptions and special cases. This task is usually something which a human or an intelligent animal can accomplish, such as learning, planning, problem-solving, etc. Follow. Upon completion of this lesson, students will be able to: 1. define 'syllable' 2. identify syllables in words Only then is the SVM capable of classifying new data. What’s an example of this? Students learn SciPi, OpenCV, and TensorFlow in this level. Let’s say we have data about supermarket transactions, where each row is a customer transaction and every column represents a different grocery item. What does it do? Imagine, our neighbor also goes to the same supermarket but only picks up soft drink, from our example we already know that people who buy soft drink also buy chips, the sales person at the counter now can succinctly push our neighbor to buy chips. Answer by Daniel Tunkelang , data scientist, search/discovery expert, led teams at … For example, if you have got a dataset of your classmates performance in class and you want to determine whether they will pass or not then CART develops a classification tree with outcomes as ‘pass’ or ‘fail’. The ratings of the items look somewhat as: “Sweetness” determines the perception of the sugar content in the items. Since we do not provide labeled class information, this is unsupervised learning. If you do a “deep learning”, the algorithms find connections between features and package them into a new single feature (in our recommendation engine example, it could be “interested in trending category”), but all in an automated way. This 5th grade science project helps explain why. Encourage kids to design and decorate their rockets first and see which one can fly the highest! The toys represent data points, and the working toys and not working toys represent two classes. What does it do? Is this supervised or unsupervised? Does it have a head and a tail? It is not possible to arrive at Y without X, and waste of time to improve X without knowing Y. A classifier is a machine learning technique that takes a bunch of data and attempts to predict which class the new data belongs to. Explaining “Deep Learning” to a 5th Grader! How did you process the object? Only when new unlabeled data is fed into it, a lazy learner tries to classify. What if things get more complicated? What’s an example of Apriori? Learners will be provided with test data sets for two use cases. Is this supervised or unsupervised? b. k-means then finds the center for each of the k clusters based on its cluster members (yep, using the characteristics of each kid in your class!). How does this information help me? Gather nonfiction books about simple machines (my Books for Teaching Simple Machines and the Inventions They Created book list is a good place to start) or reserve time in the library for student research. k-means has lots of variations to optimize and forms the clusters. For example: You want to invite Arnab but you are not sure whether he will accept your invitation or not! ... As a rule of thumb, try to explain technical concepts with analogies a 5th grader could grasp. How did this happen? A computer that can program itself is more likely to learn language faster, converse fluently, and even model human cognition. Simply explaining the concept of artificial intelligence and identifying some examples of what it might look like, does not really enable you to understand it at a deeper level.

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