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A short class with Sarah and her students. The other ways are under More.
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Good morning everyone! Today, we are going to talk about the modelling phase of the AI Project Cycle. Modelling is where we actually train our AI algorithms using data. Does anyone know why this phase is so important?
Is it because the model learns from data to make predictions?
Exactly! The model learns patterns from the data we provide, enabling it to predict outcomes later. Think of it like teaching a student with examples before testing them. Now, what are some types of AI models you think we might use?
I think there are supervised and unsupervised learning models.
And reinforcement learning!
Correct! Supervised learning uses labeled data, unsupervised learning finds patterns in unlabeled data, and reinforcement learning learns from feedback through rewards or penalties. Great job!
Let's move on to the steps involved in the modelling process. Can anyone tell me what the first step is?
Is it splitting the data into training and testing sets?
Correct! Splitting the data ensures that our model learns from one part and is evaluated on another, preventing overfitting. What do you think happens if we use the same data for training and testing?
The model might just memorize the answers instead of learning how to predict.
Right again! Next, we choose an algorithm. What factors do you think we need to consider when selecting an algorithm?
The type of data we have?
Exactly! The choice of algorithm depends greatly on the problem type and the nature of the data. After we choose the algorithm, we train the model. What do you think training involves?
Feeding the model with training data so it can learn.
Wonderful! Finally, we evaluate the model's performance using metrics like accuracy and precision. Always remember this: If the model performs well in tests, it increases confidence for real-world applications.
Now let's discuss some key concepts related to modelling, starting with overfitting and underfitting. Who can explain what overfitting means?
It means the model learns the training data too well, including noise, right?
Yes! Overfitting occurs when the model becomes too complex. On the other hand, what is underfitting?
That's when the model is too simple to capture the underlying patterns.
Exactly! Balancing complexity is key. Then we have cross-validation, which helps ensure that we are testing the model effectively. Can anyone tell me how this is typically done?
Using different subsets of data to validate the model multiple times?
Correct! This approach helps get a more accurate assessment of model performance. Finally, remember the bias-variance tradeoff, which is fundamental to understanding model accuracy.
Lastly, let’s talk about evaluation metrics. Why do we need metrics like precision, recall, and F1 score?
To see how well the model is performing on unseen data?
Correct! These metrics give us insight into how effectively our model can perform in real-world scenarios. For instance, what is the difference between precision and recall?
Precision tells us how many of the predicted positives were actually positive, while recall tells us how many of the actual positives were detected!
Exactly! Remember that F1 Score is a balance between precision and recall. The higher the F1 score, the better the model's predictive capability. Great discussion today, everyone!
The phase in AI where algorithms are trained using data to make predictions.
Includes Supervised, Unsupervised, and Reinforcement Learning.
The division of data into training and testing sets to prevent overfitting.
Tools like accuracy, precision, recall and F1 score used to assess model performance.
When modelling you choose the right way, train it hard for a great play!
Imagine a chef (AI) learning recipes (data). If he only practices one dish (overfitting), he won't cook well in a restaurant (real world).
PES (Precision, Evaluation, Splitting) for remembering key modelling steps.
STAM (Split, Train, Assess, Model) to recall the four crucial modelling steps.
Term
What is Modelling?
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What are Supervised Learning Models?
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What is underfitting?
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What does Cross-validation do?
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A type of machine learning that uses labeled datasets to train algorithms.
A type of machine learning that finds patterns in data without labeled responses.
A type of learning where an agent learns to make decisions by receiving rewards or penalties.
When a model learns noise in the training data instead of the actual signal.
When a model is too simple to capture the underlying pattern in the data.
A technique used to assess how the results of a statistical analysis will generalize to an independent dataset.
The balance between a model's capacity to minimize bias and variance in order to achieve good prediction performance.
Quantitative measures used to assess the performance of an AI model.
This section outlines the crucial steps involved in the modelling phase of the AI Project Cycle, which includes training an AI algorithm using acquired data to predict or classify future data.
In this section, students will learn about the steps involved in modelling within the AI Project Cycle. This phase is critical as it focuses on training AI algorithms with obtained data. Students will understand the different types of AI models, the importance of data splitting, algorithm selection, model training, and evaluation metrics that gauge model performance.
The modelling phase is essential within the AI Project Cycle as it dictates how an AI system learns and processes data. Here are the key components discussed in this section:
Definition: Modelling is a process where an AI algorithm is trained on cleaned and acquired data to make predictions or classify future data.
Types of AI Models:
Steps in the Modelling Process:
Key Concepts:
Each of these steps and concepts are integral to developing an effective AI model, ensuring that it performs well and is ready for real-world application.