مخطط الموضوع

  • عام

    Communication and Availability :

    Instructor: Dr.Allaoui.

    Contact: soumaya.allaoui@univ-annaba.dz. 

    Reaserchgate: https://www.researchgate.net/profile/Soumaya-Allaoui

    Linkedin: https://www.linkedin.com/in/soumaya-allaoui-55060119a/

    In-Person Availability:  Sunday and Monday from 8 AM to 12 PM at Department of Financial Sciences.

    about the course:

    • Prerequisites
    A strong foundation in mathematics (linear algebra, probability, statistics, and logic).
    • Key Learning Outcomes
    1. Improved ability to think critically, solve problems, and communicate analytical concepts.
    2. Understand the fundamental concepts and applications of Artificial Intelligence.
    3. Build ML models with NumPy and scikit-learn for regression and binary classification.Implement decision trees and ensemble methods.
    4. Apply best practices in ML development.
    5. Use unsupervised learning (clustering, anomaly detection).
    6. interpret results while evaluating model performance
    • Program Details

    • Level: Beginner (no prior ML experience required)

  • Chapter3: Principal Component Analysis (PCA)

  • هذا الموضوع

    Chapter2:to be prepared to the course

    • First TP workshop واجب

      Write a Python program using the condition statement an if...else  that does the following:

      1. Ask the user to enter the name and height of two people.

      2. Compare their heights.

      3. If one person’s height is greater than the other’s:

        • Display the name of the taller person.

        • Calculate and display the difference in height between the two people.

        • Use the function abs() to get the positive value of the difference.

      4. Print a message like: “Amir is taller than Mahdi with a difference of 20 cm.”

      Instructions of the work:

       Take clear screenshots of your Python code.

      Make sure to include your real name inside the code (for example, in a comment or print statement).

    • Hey everyone:

      Just a sweet little reminder : AS I SAID ON THE CLASS!!!! there will be a short quiz on the PCA exercise 
      during our next class on November 5th.

      Nothing stressful, I promise 🤗  it’s just to practice and make sure everyone feels comfortable with the concepts 💪 So take a quick look at your notes, get some rest, and come with your usual good vibes 😄

      Don’t forget your calculator! Phones are not allowed during the quiz.

      See you soon in class!

  • الموضوع 7

    • final project

      • Simple Dataset Description

        We have a housing dataset.
        It is used to understand the real estate market and predict house prices.

        • Target variable (what we want to predict): median_house_value (median house value)

        • Important features:

          • longitude → house longitude

          • latitude → house latitude

          • housing_median_age → median age of houses in the area

          • total_rooms → total number of rooms

          • total_bedrooms → total number of bedrooms

          • population → number of people in the area

          • households → number of households

          • median_income → median income of people in the area

        Dataset size: 10,000 to 500,000 houses
        Data types: numbers (e.g., number of rooms), categories (e.g., roof type), dates (e.g., sale date)
        Missing values: Some values may be missing (e.g., basement size for houses without a basement)

        Tasks:

        1. Load the dataset using Python.

        2. Prepare the data: handle any missing values as needed.

        3. Create a linear regression model to predict median_house_value.

        4. Evaluate the model’s performance.

        5. Briefly interpret the results and discuss which feature seems to influence house prices the most.Write clear, well-commented report ( add the code).

        Instructions:

        make plots and make detailed comments as possible 

        Bonus:

        • The best projects demonstrating creativity, additional analysis, or improved modeling will receive +3 points bonus on the final mark.