Jennie was here # Applying Statistical Concepts: Linear regression, classification, and resampling
- Description
- Learning Outcomes
- Assignments
- Contacts
- Delivery of the Learning Module
- Schedule
- Requirements
- Resources
- Folder Structure
This module introduces the skills required to design, implement and test logistic regression and classification, as well as validate it with resampling. The module compares the differences between modelling for prediction purposes and inference. It explores the choices between prediction accuracy and model interpretability, and the bias-variance trade-off.
By the end of the module, participants will be able to:
- Implement and interpret the results from several supervised learning approaches for regression and classification
- Use resampling methods to select a model
- Determine the requirements for reproducible learning
- Analyze the uncertainties associated with model results and the ethical consequences of acting on these results
- Explain the different trade offs and considerations for the statistical methods in their toolkit to both technical and non-technical audiences
Participants should review the Assignment Submission Guide for instructions on how to complete assignments in this module.
Assignment Due-dates
| Assessment | Content | Due Date |
|---|---|---|
| Assignment 1 | Sessions 1, 2, 3 | June 2 |
| Assignment 2 | Sessions 4, 5, 6 | June 9 |
| Assignment 3 | Sessions 7, 8, 9 | June 16 |
Questions can be submitted to the #cohort-3-help channel on Slack
- Technical Facilitator: Holly Xie (She/Her). Emails can be sent to xhonglei2007@gmail.com
- Learning Support Staff: Ananya Jha (She/Her). Emails can be sent to ananya.jha@mail.utoronto.ca
- Learning Support Staff: Amanda Ng (She/Her). Emails can be sent to waiyuamanda.ng@mail.utoronto.ca
- Learning Support Staff: Vishnou Vinayagame (He/Him). Emails can be sent to vishnouvina@cs.toronto.edu
This module will include live learning sessions and optional, asynchronous work periods. During live learning sessions, the Technical Facilitator will introduce and explain key concepts and demonstrate core skills. Learning is facilitated during this time. Before and after each live learning session, the instructional team will be available for questions related to the core concepts of the module. Optional work periods are to be used to seek help from peers, the Learning Support team, and to work through the homework and assignments in the learning module, with access to live help. Content is not facilitated, but rather this time should be driven by participants. We encourage participants to come to these work periods with questions and problems to work through. Participants are encouraged to engage actively during the learning module. They key to developing the core skills in each learning module is through practice. The more participants engage in coding along with the instructional team, and applying the skills in each module, the more likely it is that these skills will solidify.
The technical facilitator will introduce the concepts through a collaborative live coding session using the Python notebooks found under /01_slides. The technical facilitator will upload any live coding files to this repository for any participants to revisit under ./06_cohort_three/live_code.
| Session | Date | Topic | ISLP Chapter |
|---|---|---|---|
| 1 | May 28 | Key Concepts of Statistical Analysis | 2 |
| 2 | May 29 | Simple linear regression | 3 |
| 3 | May 30 | Multiple linear regression, interactions and Qualitative Predictors | 3 |
| 4 | June 4 | Classification vs Regression | 4 |
| 5 | June 5 | Classification (Logistic Regression) | 4 |
| 6 | June 6 | Classification (Generalized Linear Model) | 4 |
| 7 | June 11 | Resampling Methods (Leave One Out Cross Validation) | 5 |
| 8 | June 12 | Resampling Methods (K-fold Cross Validation) | 5 |
| 9 | June 13 | Resampling Methods (Bootstrap) | 5 |
- Participants are expected to have completed Shell, Git, and Python learning modules.
- Participants are encouraged to ask questions, and collaborate with others to enhance learning.
- Participants must have a computer and an internet connection to participate in online activities.
- Participants must not use generative AI such as ChatGPT to generate code in order to complete assignments. It should be use as a supportive tool to seek out answers to questions you may have.
- We expect participants to have completed the steps in the onboarding repo.
- We encourage participants to default to having their camera on at all times, and turning the camera off only as needed. This will greatly enhance the learning experience for all participants and provides real-time feedback for the instructional team.
Feel free to use the following as resources:
- Logistic Regression, explained in 3 minutes
- StatQuest: Logistic Regression
- StatQuest: Linear Discriminant Analysis (LDA) clearly explained.
- StatQuest: K-nearest neighbors, Clearly Explained
- Naive Bayes, Clearly Explained!!!
- Generalized Linear Model
- Linear Modelling
- Machine Learning Fundamentals: Cross Validation
- Bootstrapping Main Ideas!!!
- Bootstrapping Method
- $k$-fold Cross Validation
Alternative Textbook: Data Science: A First Introduction (Chapters 5-10)
.
├── 01_slides
├── 02_assignments
├── 03_exercises
├── 04_homework
├── 05_instructors
├── 06_additional_resources
├── LICENSE
├── README.md
├── requirements.txt
└── steps_to_ask_for_help.png- slides: Module slides as PDF files.
- exercises: Work to be done alongside learning sessions.
- homework: Homework to practice concepts covered in learning modules.
- assignments: Assignments.
- additional resources: Extra material not covered by the module.
- instructors: This folder provides guidance for Technical Facilitators and the Learning Support team on teaching methodologies and content delivery.
- README: This file!
- .gitignore: Files to exclude from this folder, specified by the Technical Facilitator