Syllabus

DS 362 Introduction to Machine Learning

Instructor Information

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Course Materials

Required Readings

Further Reading

R References

Course Information

Course Description

This course provides an introduction to machine learning, covering topics such as data selection, pre-processing, and transformation; supervised learning methods such as classification and regression; unsupervised methods such as clustering and dimensionality reduction; and model selection and validation. Additional relevant topics may also be covered.

Prerequisites

CMPS 240, DS 201, and DS 210

Course Overview

DS 362 builds on the foundations established in DS 201 and DS 210 to introduce students to the field of machine learning. The course will cover a variety of machine learning methods and applications. Machine learning is the basis for many of the most powerful contemporary approaches to artificial intelligence and data science. After taking this course students will be well-prepared to apply machine learning methods in a variety of contexts and to pursue further study in data science, machine learning and artificial intelligence.

Student Learning Objectives and Assessment:

Table 1: Course objectives and assessment.
Course SLO Assessment
After completing this course, students will be able to import, pre-process, and transform common data sets as appropriate for use in machine learning applications. Homework, Case Studies, and Project
After completing this course, students will be able to implement and apply foundational machine learning methods. Homework, Case Studies, and Project
After completing this course, students will be able to present and communicate results obtained via data analysis in an effective manner. Case Studies and Project

Course Policies and Procedures

Grading

Grade Policy

The overall course grade will be based on (roughly twelve) weekly homework assignment totaling 30% of the overall course grade, two data modeling case study assignments totaling 40% of the overall course grade, and a semester project totaling 30% of the overall course grade.

Grade Scale

Letter grades will be assigned based on the following scale:

Table 2: Letter grade scale.
Grade Range Letter Grade
94-100 A
90-93 A-
87-89 B+
83-86 B
80-82 B-
76-79 C+
72-75 C
69-71 C-
65-68 D+
60-64 D
<60 F

Use of AI

Artificial intelligence (AI) can be an effective tool in data science. For example, AI-based programming assistants like GitHub Copilot or generative model platforms like ChatGPT now help programmers and developers to write better code in less time. Learning to use AI is essentially becoming a basic skill for the modern data scientist. Because of this, I do not want to completely discourage the use of AI assistance.

However, I ask that you avoid using AI platforms or tools in a manner that is inappropriate in the context of this course. This course teaches a variety of concepts, skills, and critical thinking. Using AI in such a way as to avoid learning, developing skills, or critical thinking is not appropriate. If you find yourself using AI to look up answers, search for complete solutions to problems, or things like this, then your use of AI is not acceptable. It might be helpful to think of AI as an analog to a calculator. If the goal of an assignment is for you to demonstrate that you can do a certain calculation, then using a calculator is not appropriate. On the other hand, if the goal of an assignment is for you to demonstrate that you can solve a problem for which a minor step involves doing a calculation, then using a calculator is okay. AI should be treated analogously.

In particular, it is expected that students will be able to explain independently and in detail what any line of code submitted as part of an assignment this semester does. Also, it is expected that students can explain independently and in detail the solution to any problem submitted as part of an assignment this semester.

If you have any doubts about your use of AI, then either ask the instructor if your use of AI is acceptable or just don’t use AI.

Assignments

Homework Assignments

There will be roughly 12 weekly homework assignments throughout the semester totaling 30% of the overall course grade. These assignments with due dates will be posted to the course learning management system. Homework problems will be a mix of hand-written and computer assignments and the problems will relate to the material covered in lectures and readings.

Data Modeling Case Studies

Over the course of the semester, you will be asked to complete two data modeling case studies that will form a total of 40% of the overall course grade. For each case study, you will be provided with a data set and then asked to apply and compare multiple methods of analysis on the provided data set. A complete data modeling case study should consist of

  • Appropriately documented code implementing appropriate models.

  • A GitHub repository with all code.

  • A report developed in Quarto that summarizes your conclusions.

Further details on the case study assignments such as the parameters of the assignments and grade rubrics will be posted on the course learning management system.

Semester Project

The semester project will incorporate all the components of a data analysis covered in the course throughout the semester applied to a particular data set. Various components of the project will be due at different times but you will have the opportunity to revise some components prior to the submission of the final product.

A complete project, counting for 30% of the overall course grade will consist of the following:

  • An initial exploratory data analysis for your data set.

  • An appropriate analysis of your data set with the goal to address a specific research question.

  • A project report developed using Quarto.

  • A GitHub repository containing all code (appropriately documented) written and used in your project.

Your project report and presentation should be written as if it is addressed to a stake holder with some subject matter knowledge in the domain of application but not necessarily with a quantitative or programming background. Further details on the semester project such as the parameters of the assignment and a grade rubric will be posted on the course learning management system.

Course Timeline

Weekly Schedule

  • Week 1: Review of background concepts

  • Week 2: Data mining

  • Week 3: Machine learning

  • Week 4: Regression

  • Week 5: Classification; Project component 1 due

  • Week 6: Resampling methods

  • Week 7: Model selection and regularization; Case Study 1 due

  • Week 8: CARTs

  • Week 9: Support vector machines

  • Week 10: Neural Networks

  • Week 11: Deep learning; Project component 2 due

  • Week 12: Association rule learning

  • Week 13: Unsupervised methods; Case Study 2 due

  • Week 14: Reinforcement

  • Week 15: Ethical considerations; Project final version due

Important Dates

Table 3: Important dates.
Event Date
Classes begin
Last day to add classes
Holiday, no classes
100% tuition refund
Drop (no grade)
Fall break to
Mid-semester
Withdraw with W
Thanksgiving break to
Last week to
Finals to

University Resources for Students and Academic Honesty

Students with Disabilities

Reasonable academic accommodations may be provided to students who submit relevant and current documentation of their disability. Students are encouraged to contact the Center for Teaching and Learning Excellence (CTLE) at or (570) 941-4038 if they have or think they may have a disability and wish to determine eligibility for any accommodations. For more information, please visit http://www.scranton.edu/disabilities.

Writing Center Services

The Writing Center focuses on helping students become better writers. Consultants will work one-on-one with students to discuss students’ work and provide feedback at any stage of the writing process. Scheduling appointments early in the writing progress is encouraged.

To meet with a writing consultant, call (570) 941-6147 to schedule an appointment, or send an email with your available meeting times, the course for which you need assistance, and your phone number to: . The Writing Center does offer online appointments for our distance learning students.

Academic Honesty and Integrity

Each student is expected to do their own work. It is also expected that each student respect and abide by the Academic Code of Honesty as set forth in the University of Scranton student handbook. Conduct that violates the Academic Code of Honesty includes plagiarism, duplicate submission of the same work, collusion, providing false information, unauthorized use of computers, theft and destruction of property, and unauthorized possession of tests and other materials. Steps taken in response to suspected violations may include a discussion with the instructor, an informal meeting with the dean of the college, and a hearing before the Academic Dishonesty Hearing Board. Students who are found to have violated the Code will ordinarily be assigned the grade F by the instructor and may face other sanctions. The complete Academic Code of Honesty is located on the University website at https://www.scranton.edu/academics/wml/acad-integ/acad-code-honesty.shtml.

My Reporting Obligation as a Responsible Employee

As a faculty member, I am deeply invested in the well-being of each student I teach. I am here to assist you with your work in this course. Additionally, if you come to me with other non-course-related concerns, I will do my best to help. It is important for you to know that all faculty members are required to report incidents of sexual harassment or sexual misconduct involving students. This means that I cannot keep information about sexual harassment, sexual assault, sexual exploitation, intimate partner violence or stalking confidential if you share that information with me. I will keep the information as private as I can but am required to bring it to the attention of the University’s Title IX Coordinator, Elizabeth M. Garcia, or Deputy Title IX Coordinator, Diana M. Collins, who, in conversation with you, will explain available support, resources, and options. I will not report anything to anybody without first letting you know and discussing choices as to how to proceed. The University’s Counseling Center (570-941-7620) is available to you as a confidential resource; counselors (in the counseling center) do not have an obligation to report to the Title IX Coordinator.

Non-discrimination Statement

The University is committed to providing an educational, residential, and working environment that is free from harassment and discrimination. Members of the University community, applicants for employment or admissions, guests, and visitors have the right to be free from harassment or discrimination based on race, color, religion, ancestry, gender, sex, pregnancy, sexual orientation, gender identity or expression, age, disability, genetic information, national origin, veteran status, or any other status protected by applicable law.

Students who believe they have been subject to harassment or discrimination based on any of the above class of characteristics, or experience sexual harassment, sexual misconduct or gender discrimination should contact Elizabeth M. Garcia, Title IX Coordinator, (570) 941-6645 , Deputy Title IX Coordinators Diana M. Collins (570) 941-6645 , or Ms. Lauren Rivera, AVP for Student Life and Dean of Students, at (570)941-7680 . The United States Department of Education’s Office for Civil Rights (OCR) enforces Title IX. Information regarding OCR may be found at <www.ed.gov/about/offices/list/ocr/index.html>

The University of Scranton Sexual Harassment and Sexual Misconduct Policy can be found online at https://www.scranton.edu/diversity. All reporting options and resources are available at https://www.scranton.edu/CARE.

About Pronouns

It is easy to make assumptions about an individual’s pronouns, but we try not to! Please tell us in class or via a private email if you would like to let us know what your pronouns are, if/when you would like us (and others) to use them, and certainly feel free to correct us or others if we make a mistake. Using the pronouns that a person has indicated they prefer is considered both professional and polite, and as such we ask that all members of our class use the appropriate pronouns.

If you have questions about this, please feel free to look up more information here (https://www.mypronouns.org/) or email with any questions.

Student Mental Health: Suggestions and Resources

Many students experience mental health challenges at some point in college. Struggles vary and might be related to academics, anxiety, depression, relationships, grief/loss, substance abuse, and other challenges. There are resources to help you and getting help is the smart and courageous thing to do.

  • Counseling Center (6th Floor O’Hara Hall; 570-941-7620) – Free, confidential individual and group counseling is available on campus.

  • Teletherapy – For students who wish to access therapy via video, phone, and/or chat, the University offers a teletherapy resource. Please contact the Counseling Center (570-941-7620) to inquire about teletherapy.

  • Mental Health Screenings – Confidential, online “check up from your neck up” to help you determine if you should connect with a mental health professional.

  • Dean of Students Office (201 DeNaples Center; 570-941-7680) – Private support and guidance for students navigating personal challenges that may impact success at the University

Final Note

The instructor reserve the right to modify this syllabus; students will immediately be notified of any such changes and an updated syllabus will be made available to the class via the course learning management system.