Team Evaluation (Fusion Coding)

Team Evaluation: Fusion Coding

This is a team evaluation session where each team solves a quiz.
  • Let's use what we've learned so far to find the answer to the quiz.
  • It's time to find the answer to some of the quizzes below.
  • After answering the team quiz, there will be presentations by each team.
Topic:
  1. Fusion Coding essential questions

    1. Array Creation: How can you create a Numpy array from a Python list?

    2. Image Loading: How can you load an image using OpenCV, and what data structure is used to represent the image?

    3. Color Spaces: Explain the significance of converting an image to different color spaces in OpenCV.

    4. Video Processing: Discuss the steps involved in processing a video stream using OpenCV.

    5. File Saving in OpenCV: How can you save an image or video file using OpenCV, and what are the common file formats supported?

    6. Scikit-Learn Basics: How do you split a dataset into training and testing sets using Scikit-Learn?

    7. Event Handling: How does event handling work in PyQt, and how can you capture and respond to button clicks?

    8. Custom Widgets: How can you create a custom widget in PyQt, and what are the benefits of doing so?

    9. Data Binding in PyQt: Explain the concept of data binding in PyQt, and how does it enhance the synchronization between the application’s user interface and underlying data structures?

    10. PyQt Styling: How can you apply stylesheets to PyQt widgets for customizing the look and feel of the application, and what considerations should be taken into account for a consistent design?

  2. Fusion Coding choice questions

    1. Array Indexing: How do you index a 2D Numpy array to access a specific element?

    2. Object Detection: What are Haar cascades, and how are they utilized for object detection in OpenCV?

    3. OpenCV Contour Detection: How is contour detection implemented in OpenCV, and what are its applications in image processing, such as object recognition or shape analysis?

    4. Linear Regression: Explain the concept of linear regression, and how is it implemented in Scikit-Learn?

    5. Support Vector Machines (SVM): What is the role of the kernel in SVM, and how does it affect decision boundaries?

    6. Combining PyQt and Data Analysis: How can you integrate PyQt with data analysis tools to create interactive GUI applications?

    7. PyQt Signals and Slots: What is the role of signals and slots in PyQt, and how do they enable communication between objects?

    8. GUI Optimization in PyQt: How can you optimize the performance of a PyQt GUI application, especially when dealing with large datasets?

    9. Data Visualization in PyQt: How can you integrate data visualization libraries, like Matplotlib or Plotly, into a PyQt application to create interactive plots and charts?

    10. PyQt Threading: What are the challenges of implementing multithreading in a PyQt application, and how can it be utilized for background tasks or improving responsiveness?

Evaluation Standard:

  1. Choose one Fusion Coding essential question and one Fusion Coding choice question to submit your report.

  2. Write a report about what you felt after watching the video.

  3. The report evaluation criteria are as follows.

    • A+ : Write at least 20 pages

    • A : Write at least 15 pages

    • B+ : Write at least 10 pages