CSE 450 Course Overview
Welcome to CSE 450 - Machine Learning and Data Mining.
Course Objectives
- Apply machine learning algorithms to poorly structured data to discover meaningful patterns.
- Explain the approaches and appropriate applications of a broad set of machine learning algorithms.
- Articulate, in professional terms, the concepts, vocabulary, limitations, and framework of machine learning.
- Explain the preparation required for various machine learning career paths.
The goal of these objectives is to help you:
- Be excited about machine learning and data mining.
- Have the ability to tackle a real dataset and do something interesting with it.
- Be prepared to start a graduate level experience, class, or project in machine learning / data mining.
- Understand current challenges and limitations in the field.
Course Textbook
The textbook for this course is Fundamentals of Machine Learning for Predictive Data Analytics by Kelleher, Mac Namee, and D'Arcy; 1st Edition.
You may access this text book online for free via Proquest, courtesy of the BYU-Idaho library.
Course Structure
This course is divided into seven modules. Each module will last approximately two weeks (six class periods for campus students). Most of these modules, modules one through six, will be completed as teams.
Subject to Change
Keep in mind that your instructor may deviate somewhat from the following guide, and they have final say on assignment requirements, delivery methods, and due dates. So be sure to pay attention to both in-class, MS Teams, and Canvas announcements.
Time Expectations
University guidelines suggest that students spend an average of three hours per week per credit hour.
While there will be some time provided in class for team assignments for campus students, as this is a three credit course, the expectation is that students will spend an average of six hours per week on assignments outside of class, including the time spent on preparation readings.
Each module is worth a percentage of your final grade:
Module 01
Module 01 is designed to quickly bring you up to speed on the foundational knowledge and vocabulary you will need to succeed in the rest of the course.
Preparation Reading Quizzes
For each data exploration in Module 01, you will complete a Preparation Reading, followed by an associated quiz.
You may take each quiz as many times as you like until its due date. You must score at least 90% on a quiz to receive credit for it.
If you take a quiz multiple times, the highest passing score you receive will be your final score for that quiz.
At the start of class for campus students, the instructor will briefly review the material and answer questions from the reading, but will assume that you have already completed the reading. Online students may send questions to the instructor or other classmates after completing the reading.
Data Explorations
After reviewing the reading, you will work in temporary groups during class for campus students to complete hands-on, Data Exploration assignments. Online students will use Microsoft Teams meetings.
Data Exploration Evaluations
In order to receive credit for a Data Exploration, each student must fill out a Data Exploration Evaluation in Canvas, according to the instructions provided.
Individual Exploration
At the end of Module 01, you will complete an Individual Exploration assignment that will test your Module 01 knowledge as well as your ability to apply that knowledge through a series of exercises.
This assessment will be done outside of class and should take approximately two hours to complete.
Its main purpose is to help you decide if you are adequately prepared to continue in the course.
Students who elect to continue with the course will be assigned to permanent teams for the remainder of the semester.
Modules 02 - 06
Modules 02 - 06 use case studies to help students learn to understand and apply a variety of machine learning techniques.
Each module lasts approximately two weeks and follows a case-study format. Each case study is designed to provide students with a real world backdrop to learn and apply machine learning tools.
Students will work in teams to create a report designed to answer the questions posed by the case study. Students will then present their work to the class. Online students will post videos and campus students will present in class. Students will then have an opportunity to reflect on their own and their teammates performance and contributions.
Modules 07
Module 07 provides an opportunity for students to apply machine learning techniques to a personally selected project.
Students will have a week to work individually on a dataset of their choice. Students will demonstrate their ability to work with a dataset from start to finish, using the skills aquired in the course. Students will select a dataset, pre-process the data, generate visuals, apply machine learning techniques, evaluate findings, and write an executive summary as a final project.
Grading
See your Canvas section for exact grade weights for each assignment, but in general, assignments are weighted similar to the following:
- Module 01 - Machine Learning Introduction: 10%
- Module 02-06 - Case Study Preparation: 10%
- Module 02-06 - Case Study Executive Summaries: 40%
- Module 02-06 - Case Study Personal Contribution: 20%
- Module 02-05 - Case Study Holdout Predictions: 10%
- Module 07 - Individual Case Study: 10%
Final Letter grades are given according to the standard BYU-Idaho grading scale.
Late Policy
Because of the team-based, interactive nature of this course, no late credit is given.
In the event of an extreme emergency that prevents you from communicating with your team, your instructor will work with you to come up with a way for you to make up the content of a given module.
Note that the following are not considered extreme emergencies:
- Marriages, Family Reunions, Bar Mitzvahs, Quinceaneros, Temple Trips, or other celebratory events.
- Having to work
- Oversleeping
- A broken or malfunctioning computer, Internet connection, or other technical issue.
- Suffering from any illness that keeps you home but still allows you to reach out via Teams, Zoom, or Slack
- Being quarantined
Student Support
Support is available in many ways including via other class members and discussion in Slack. In addition, help is available through the university's academic support center.
Dress and Grooming
You are expected to follow the university's Dress and Grooming Standards
This includes any current university requirements and/or guidelines related to wearing masks and/or social distancing.
Preventing Sexual Misconduct
BYU-Idaho prohibits sex discrimination by its employees and students in all of its education programs or activities. This includes all forms of sexual harassment, such as sexual assault, dating violence, domestic violence, stalking, conditioning a grade or job on participation in sexual conduct, and other forms of unwelcome sexual conduct.
As an instructor, one of my responsibilities is to help create a safe learning environment for my students and for the campus as a whole. University policy requires deans and department chairs, and encourages all faculty, to report every incident of sexual harassment that comes to their attention. If you encounter or experience sexual harassment, please contact the Title IX Coordinator at titleix@byui.edu or 208-496-9209. Additional information about sex discrimination, sexual harassment, and available resources can be found at www.byui.edu/titleix
Disability Services
BYU-Idaho does not discriminate against persons with disabilities in providing its educational and administrative services and programs and follows applicable federal and state law. This policy extends to the University’s electronic and information technologies (EIT).
Students with qualifying disabilities should contact the Disability Services Office at disabilityservices@byui.edu or 208-496-9210. Additional information about Disability Services resources can be found at http://www.byui.edu/disabilities.
Academic Honesty
“When you are honest in every way, you are able to enjoy peace of mind and maintain self-respect. You build strength of character, which allows you to be of service to God and others. You are trustworthy in the eyes of God and those around you. If you are dishonest in your words or actions, you hurt yourself and often hurt others as well. If you lie, steal, cheat, or neglect to give the full amount of work for your pay, you lose your self-respect. You lose the guidance of the Holy Ghost” (“Honesty,” True to the Faith (2004), 84)
Academic Honesty means students do their own work. This also means their instructors will evaluate that work. Students should not be dishonest—this includes all types of work in their courses. The complete Academic Honesty Policy can be found at http://www.byui.edu/student-honor-office/ces-honor-code/academic-honesty.
Academic Grievances
Students are encouraged to contact their instructors regarding course-related concerns. If concerns cannot be resolved in this way, students may contact the BYU-Idaho Support Center. to formally register a concern or grievance. The Student Grievance Policy. can be found here.
Changes to Schedule and Assignments
Schedules, assignments, and policies are subject to change. You will be notified of any changes on I-learn.
Data Sources
This course uses datasets in the public domain or under a compatible license. Citations and other information for each dataset may be found on the course data sources page.