Midterm Presentation I - Regression

MSSC 6250 Machine Learning, Spring 2025

Author

Dr. Cheng-Han Yu

Modified

April 1, 2025

Timeline and Things to Do

  • Team up! You will be working as a group of 3. One of you, please email me your team member list by Friday, 2/21 11:59 PM

  • Proposal. Please send me a one-page PDF describing what you are going to do for your project (no word limit) with your project title by Friday, 2/28 11:59 PM.

  • Presentation. You will be presenting your project on Thursday, 3/6 in class.

  • Materials. Please share your entire work (slides, code, data, etc) by Friday, 3/7 11:59 PM.

Policy

Team up!

  • Each one of you loses 5 points of your project grade if you don’t meet the requirement or miss the deadline.

  • You will be randomly assigned to a group if you do not belong to any group before the deadline.

Proposal

  • Each one of you loses 5 points of your project grade if you don’t meet the requirement or miss the deadline.

  • Your proposal (in PDF) should include three parts:

    • Project title
    • The goal of your project. For example, what is the research question you’d like to answer? What machine learning method/model/algorithm you’d like to introduce? What data you’d like to use for analysis or demonstration? etc.

  • Although it is risky, you can change your project topic after you submit your proposal if you decide to do something else.

Presentation

  • Each group presentation should be between 10 and 11 minute long, followed by 1 to 2 minute Q&A. If your presentation is too short or too long, every one of you loses 5 points of your project grade.

  • Every group member has to present some part of the group work. The one who does not present receives no point.

  • Questions are encouraged during Q&A. Everyone is welcome to ask any questions about the projects.

Materials

  • Each one of you loses 5 points of your project grade if you don’t meet the requirement or miss the deadline.

  • You need to share your entire work, including slides, code, and data if applicable.

  • Your code should be able to reproduce all the numerical results, outputs, tables, and figures shown in the slides, including the source of the raw data (where you find and load the data) if the project is about data analysis.

Project Content

Your project can be in either of the following categories:

  1. (DA) Data analytics using one or more REGRESSION methods.

  2. (MA) Introduce a new REGRESSION model/method/algorithm not lectured in class.

Data Analytics

For your DA project, you need to

  • Describe the selected data set.

  • Explain and show why the chosen model(s) is appropriate for answering your research questions and better than others.

  • Interpret your analysis result.

Below are some data repositories you can start with, but you are encouraged to explore more and find your favorite one.

Model/Algorithm

For your MA project, you need to

  • Describe the intuition and idea of the method. What are the pros and cons of the method?

  • Provide the mathematical expression of the model/algorithm. Explain the model and its properties, and how we do supervised learning with the model/algorithm.

  • Compare the chosen method with other methods learned in class. Determine which method performs better under what conditions.

  • Demo how to implement the method using a programming language for supervised learning.

Project Evaluation and Grading

  • Your project performance grade is determined by your classmates and Dr. Yu.

  • Table 1 shows your possible performance grade. For example, if your group finish in second place (Silver) you get 145 points.

  • Your project grade will be

project grade = performance grade - points lost due to violation of policy

Performance Grade Sheet
Performace Grade 🎖
Gold 🥇 1st 150
Silver 🥈 2nd 145
Bronze 🥉 3rd 140
Other teams 135

Group Performance Evaluation

  • You will need to evaluate all group projects except the one you work on.

  • You evaluate group performance based on the rubric attached. Four evaluation criteria are considered:

    • Project Content and Organization (8 pts)
    • Presentation Material (Slides) Quality (4 pts)
    • Oral Presentation Skill and Delivery (4 pts)
    • Interactions and Q&A (4 pts)

  • The total points of a project presentation is 20 points.

  • Evaluation sheets will be provided on the presentation day.

How do you get the full points for each category? Check the rubric below.

  • Content and Organization (DA)

    • Beautiful visualization helps find out relationship of variables and specification of models
    • All questions are answered accurately by the models
    • Discuss how and why the models are chosen
    • Apply sophisticated models and detailed analysis
    • All ideas are presented in logical order

  • Content and Organization (MA)

    • Explain the method clearly and accuratly
    • Show the pros and cons of the method, and compare with the methods learned in class.
    • Show how the method can be implemednted for supervised learning.
    • All ideas are presented in logical order

  • Presentation Material Quality

    • Presentation material show code and output beautifully
    • Presentation material clearly aid the speaker in telling a coherent story
    • All tables and graphics are informative and related to the topic and make it easier to understand
    • Attractive design, layout, and neatness.

  • Oral Presentation Skill

    • Good volume and energy
    • Proper pace and diction
    • Avoidance of distracting gestures

  • Interactions and Q&A

    • Good eye contact with audience
    • Excellent listening skills
    • Answers audience questions with authority and accuracy

  • After you evaluate all group project presentations, you rank them from 1st to last based on their earned points.

  • No two groups receive the same ranking. If you give two or more groups some points, you still need to give them a different ranking, deciding which team deserves a higher ranking according to your preference.