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A short class with Sarah and her students. The other ways are under More.
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Good afternoon class! Today, we will explore the concept of modelling in Artificial Intelligence. Can anyone tell me what they think modelling means?
Is it like creating something based on real-world examples?
That's right! Modelling is about creating representations of real-world scenarios so that machines can learn from them. Think of it like how we create models in our minds to solve problems.
What kind of data do we collect for modelling?
Great question! We collect data that is relevant to the scenario we are modelling, such as features and labels. Does anyone remember what features might be when modelling fruits?
Color, weight, and shape could be features!
Exactly! These features help the machine learn to differentiate, for instance, between apples and oranges. Remember the acronym P.A.C.T. - Patterns, Analysis, Collection, Training to help you recall the core steps of modelling.
Can we use modelling for more than just identifying fruits?
Absolutely! Modelling can be applied to various fields like healthcare or finance. At the end of the day, modelling is about making sense of data to learn and make decisions.
Now that we understand what modelling is, let’s break down the process of creating a model. What's the first step?
Isn’t it identifying the problem we want to solve?
Exactly! Problem identification is crucial. Next, we need to gather relevant data. What should we consider when collecting data?
The data should be clean and relevant to the problem.
Yes! Clean data is essential for training a good model. Then, we preprocess the data. Does anyone know what 'data preprocessing' involves?
Maybe cleaning or normalizing it?
Spot on! It's about ensuring the data is ready for the model. Next, we select our model or algorithm. What examples do we have?
I remember Linear Regression and Decision Trees!
Correct! Next, we train the model. We feed it the data we've prepared, then test it to see how well it learned. Finally, we deploy the model in real-world applications. Remember the acronym D.P.A.T.T. - Data, Preprocessing, Algorithm, Training, Testing - to recall the steps!
Let’s now discuss the types of modelling used in AI. Who can share how descriptive modelling and predictive modelling differ?
Descriptive modelling is about understanding past data, right?
Exactly! Descriptive modelling finds patterns within existing data. And what about predictive modelling?
It focuses on forecasting future outcomes!
Right again! Predictive modelling uses past data to make future predictions. Can you think of real-life applications for each?
Descriptive could be used in market segmentation, and predictive in predicting house prices?
Perfect examples! Remember the mnemonic P.E.R. for Predictive, Explore, Result to help you distinguish the purposes of each type.
The essential process through which AI systems learn from data.
A focus on analyzing past data to find patterns.
Aimed at forecasting future events based on historical data.
The methods used to prepare raw data for meaningful interpretation.
When you model, remember PACT; Patterns, Analysis, Collection, Training is a fact!
Imagine a gardener who wants to grow the best apples. He collects soil samples (data), analyzes them (patterns), prepares the ground (structure), and plants the seeds (training). Soon he can tell by sight which trees will produce the biggest apples (predictions)!
Use D.P.A.T.T. – Data, Preprocessing, Algorithm, Training, Testing, Deployment to remember the steps in modelling.
Remember P.E.R. for Predictive, Explore, Result for types of modelling.
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Modelling
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Descriptive Modelling
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Predictive Modelling
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Data Preprocessing
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The process of creating mathematical or logical representations to help machines learn from data.
The process of gathering relevant data to train a model.
Identifying trends and correlations in collected data.
A mathematical method or formula used to train a model.
The process of feeding data into a model for it to learn from.
Evaluating a model's performance on unseen data.
A type of modelling that focuses on analyzing past data.
A type of modelling aimed at predicting future outcomes based on past data.
The techniques used to clean and prepare data for model training.
Modelling in AI involves representing real-world scenarios mathematically to enable machines to learn and make predictions.
Modelling is critical in AI as it allows systems to learn from data. This process includes data collection, pattern analysis, building mathematical structures, and training machines to predict outcomes, akin to how humans learn from examples.
In Artificial Intelligence, modelling is an essential process that translates real-world scenarios into mathematical or logical representations, enabling machines to learn and make informed predictions or decisions. It consists of several key steps: data collection, where relevant information is gathered; pattern analysis, which identifies trends in the data; the construction of logical or mathematical frameworks; and training the machine to recognize or predict outcomes based on the gathered data. An example of modelling would be training a model to differentiate between apples and oranges using features like color and weight, allowing the model to make classifications based on learned differences. Understanding modelling's importance, types, and processes is vital for AI development, as it underpins effective learning and automation.