Introduction to AI Project Cycle

Today we’ll start with the foundation of the AI project cycle, called Problem Scoping. Can anyone tell me what we mean by problem scoping?

Is it about figuring out what problem we want to solve?

Exactly! It's all about clarifying the objective. We need to ask: What do we want to achieve? Remember the acronym P.O.S.E.: Problem, Objective, Stakeholders, and Evaluation criteria.

What do you mean by stakeholders?

Great question! Stakeholders are individuals or groups affected by the problem and solution. They can include users, customers, and organizations. Now, how do constraints affect our problem approach?

Constraints might limit our budget or time, right?

Exactly right! Now, who remembers a success criterion? Can someone give an example of one?

If we want to reduce food waste, it could be reducing leftovers by a certain percentage?

Perfect! Let’s summarize: Problem Scoping involves defining the problem, understanding stakeholders, identifying constraints, and determining how to measure success.

Now that we've scoped the problem, let’s discuss Data Acquisition. Why do you think data is crucial in the AI cycle?

Because it helps us understand the problem better?

Absolutely! Data is our foundation. We can gather data from various sources, like surveys and public datasets. Can anyone think of a specific source?

Maybe social media or government databases?

Exactly! Now, what about the types of data? Can someone distinguish between structured and unstructured data?

Structured is like spreadsheets, while unstructured is things like photos or text?

Spot on! We need both types. Let’s wrap up: Data Acquisition is about collecting diverse data from various sources to inform our project.

Next, we move to Data Exploration. Why do you think we need to explore data before diving into modeling?

To ensure the data is clean?

Exactly! We perform Exploratory Data Analysis, or EDA. It’s about cleaning, visualizing, and understanding our data. What are some tasks of EDA?

Removing errors or visualizing data to see patterns?

Right! We want to find insights early. For example, if we notice trends, like high food wastage on certain days, it’s vital for modeling. So, what tools might we use in EDA?

Excel or Python?

Correct! In summary, Data Exploration is crucial for preparing clean and insightful data for our models.

Let’s talk about the heart of the project: Modelling. What do you think happens during this phase?

We create the model that learns from the data?

Correct! This is where we build models using either supervised or unsupervised learning. Who can give examples of each?

Supervised could be predicting housing prices, while unsupervised is clustering data?

Exactly! Once we have our data. We will split it into training and testing sets. Why do we do that?

To see how well our model performs on new data?

Correct! In conclusion, Modelling is about building our predictive models and validating their performance.

Finally, let’s evaluate our model. Why is evaluation crucial after modeling?

To know if our model works effectively?

Exactly! We look at metrics like accuracy and precision. Can anyone explain what a confusion matrix shows?

It shows the true positives and negatives compared to false ones?

Correct! If our model doesn’t perform well, what can we do?

We could improve the data quality or try a different algorithm?

Exactly! In summary, Evaluation is essential to measure model performance and optimize it for better predictions.