Introduction to Statistical Learning
Spring 2024 Block 8
Instructor
Instructor Dr. Tyler George
Cornell College, West 311
Class Meetings
April 15th - May 8th
9am-11am & 1pm-2pm
West 201
Office Hours
MWTh 3:05pm-4:05pm and by appt.
West 311
I am available far beyond these times listed. Please email me and we can set up a time to chat about class material or whatever you prefer! I will generally announce changes to office hours in class but I still suggest checking the Course Calendar to verify availability.
You Are A Priority
My goal this block is to help you learn the material. I want to first and foremost recognize that you are an individual and thus are unique and may learn uniquely. Additionally, your health and wellbeing are priority one. Learning cannot happen effectively if you don’t meet your other personal needs. That all being said, I have structured the class in a way that I, from experience teaching and learning myself, think will be most beneficial for the majority of students. I promise you that I will do my best to create an inclusive and engaging learning environment. I ask that you keep an open line of communication between us for when you may need help and/or flexibility. You and your learning are why I am here.
Course Description
This course will introduce students to relatively new and powerful statistical techniques used to analyze data. The course will begin with a review of linear regression and an introduction to computer-based variable and model selection methods. Other topics will include classification methods, resampling methods for model-building, non-linear models, and tree-based methods. The computer software program R will be used throughout.
Learning Objectives
At the end of this course I would like you conceptually understand, be able to use apply, and interpret the results of
- The variance/bias trade-off
- Measuring quality of fits
- Linear model selection and regularization
- Classification including K nearest neighbors
- Cross validation
- Dimension reduction
- Tree based methods
- Unsupervised learning
Prerequisite
To be successful in this class, you should have completed STA 201, STA 202, and DSC 223.
Open Access Books – Free!
All of materials for this class are free.
The main text book is: An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani – it is freely available online.
The secondary text is R resource: Tidy Modeling with R by Max Kuhn and Julia Silge – it is freely available online.
Course Site and Moodle
Our course will run from a combination of Moodle and the course website at https://sta362-sb8-24.github.io/STA362StatLearning/.
Software – No need to install
We will use a combination of technologies in this course including R, and RStudio (server). Luckily for you I have put lots of effort into setting all of this on a machine we have on campus that we will all access with a web browser! You don’t need to install any – in fact for a while I prefer you don’t. More on this in class. If you are an off campus student, please let me know right away, as you may need to checkout a laptop (free) from IT to work on homework from home.
If you have any technical problems you should contact IT as soon as possible. Submit a Work Order!
Group Work
In this class, I would like you to work in groups for a variety of reasons. A large part of this class is communicating analysis – not just completing analysis. At the beginning of the block, groups will be formed. You should expect to work with this group every day. When we work in groups in class we will decide on roles, specifically who is controlling the one screen will rotate). Group members will rotate roles between tasks to help make sure everybody is sharing work. You won’t be working in a group for everything; any quizzes, and exams may be individual.
Evaluations and grades
Grade Category Descriptions
Homework:
Homeworks will be graded for correctness. The goal is the practice the application of the method and then be able to interpret the result.
Participation
This will be measured by attending class and working on the work given including labs and class examples.
Project
This will entail multiple stages of submission with details accessed through “Project” on the left side of the course website (once available). Some class time will be given for discussing projects with me but not enough to complete the project during class times. I do not anticipate we will start these until week 3.
Exams
There will be a Midterm exam (4/26) and a final exam (morning of 5/8).
| Assignment | Points |
|---|---|
| Homework | 200 |
| Participation | 100 |
| Project | 300 |
| Exams, two 200pts exams | 400 |
| Total | 1000 |
| Grade | Range | Grade | Range |
|---|---|---|---|
| A | 93-100% | C | 73-76% |
| A- | 90–92% | C- | 70-72% |
| B+ | 87–89% | D+ | 67-69% |
| B | 83-86% | D | 63-66% |
| B- | 80-82% | D- | 60-62% |
| C+ | 77-79% | F | <60% |
Use of AI
I expect you to generate your own work in this class. When you submit any kind of work (including projects, exams, homeworks), you are asserting that you have generated and written the text, and code, unless you indicate otherwise by the use of quotation marks and proper attribution for the source. Submitting content as your own that has been generated by someone other than you, or was created or assisted by a computer application or tool, including artificial intelligence (AI) tools such as ChatGPT is cheating and constitutes a violation of our Academic Honesty policy. You may use simple word processing tools to update spelling and grammar in your assignments, but unless given permission otherwise, you may not use AI tools to draft your work, even if you edit, revise, or paraphrase it. There may be opportunities for you to use AI tools in this class. Where they exist, I will clearly specify when and in what capacity it is permissible for you to use these tools.
DISABILITIES AND ACCOMODATIONS POLICY
Cornell College makes reasonable accommodations for persons with disabilities. Students should notify the Office of Academic Support and Advising and their course instructor of any disability related accommodations within the first three days of the term for which the accommodations are required, due to the fast pace of the block format. For more information on the documentation required to establish the need for accommodations and the process of requesting the accommodations.
ACADEMIC HONESTY POLICY
Cornell College expects all members of the Cornell community to act with academic integrity. An important aspect of academic integrity is respecting the work of others. A student is expected to explicitly acknowledge ideas, claims, observations, or data of others, unless generally known. When a piece of work is submitted for credit, a student is asserting that the submission is her or his work unless there is a citation of a specific source. If there is no appropriate acknowledgment of sources, whether intended or not, this may constitute a violation of the College’s requirement for honesty in academic work and may be treated as a case of academic dishonesty. The procedures regarding how the College deals with cases of academic dishonesty appear in The Catalog, under the heading “Academic Honesty.”
Illness Policy
If you are experiencing COVID-19 symptoms, do not attend class. Perform a home test or contact Director of Student Health Services Lynn O’Brien at student_health@cornellcollege.edu immediately to arrange a COVID-19 test at the Health Center. If you need to isolate due to COVID-19, or if you become unable to attend class for any other health reason, contact me as soon as possible to determine if you are able to continue in the class. A Withdrawal for Health Reasons may be required.
Mandatory Reporter Reminder
It is my goal that you feel supported and able to share information related to your life experiences during classroom discussions, in your written work, and in any one-on-one meetings with me. You should also know that all Cornell College faculty and staff are mandatory reporters. This means that I will keep information you share with me private to the greatest extent possible. However, I am required to share information regarding sexual assault, abuse, criminal behavior, or about a student who may be a danger to themselves or to others. If you wish to speak to someone confidentially who is not a mandatory reporter, you can schedule an appointment with one of the counselors in the Ebersole Health and Wellbeing Center or contact the College Chaplain, Rev. Melea White, at mwhite@cornelllcollege.edu.