Revised: Basic information

  • Course title: SDS 293: Modeling for Machine Learning
  • Instructor: Albert Y. Kim - Assistant Professor of Statistical & Data Sciences
  • Email: Slack team: Click hashtag icon in navbar for the browser interface or use the desktop app.
  • Getting help:
    1. Ask questions in the #questions channel on Slack.
    2. I will be “live answering” questions in the #questions channel on Slack at the following times:
      • MWF 9:30am - 10:30am EDT. My goal is to have lectures posted by 9:30am.
      • TuTh 4:15pm - 5:00pm EDT
    3. If a particular question gets too involved to explain over Slack, we’ll jump into a Zoom room as needed.
    4. For more private discussions, please book an appointment at bit.ly/meet_with_albert. If none of the times work for you or does not align with your timezone, please Slack me.

Instructor work-life balance

  • I will respond to Slack messages sent during the week within 24h. I will respond to Slack messages sent during the weekend at my own discretion.
  • If possible, please only Slack me with briefer and administrative questions; I prefer having more substantive conversations in person as it takes me less energy to understand where you are at.
  • I will do my best to return all grading as promptly as possible.

How can I succeed in this class?

Ask yourself:

  • When I have questions or don’t understand something:
    • “Am I asking questions in class?”
    • “Am I asking questions on Slack in #questions?” Even better: “Am I answering my peers’ questions on Slack?”
    • “Having I been going to the Spinelli tutoring center for help on R and the tidyverse?”
    • “Have I been coming to office hours?”
  • Lectures:
    • “Am I staying on top Slack notifications sent between lectures?” If you need help developing a notification strategy that best suits your lifestyle, please speak to me.
    • “Am I attending lectures consistently?”
    • “Am I actually running the code and studying the outputs in R during in-class exercises, or am I just going through the motions?”
    • “During in-class exercises, am I taking full advantage that I’m in the same place at the same time with the instructor, the lab assistants, and most importantly your peers, or am I browsing the web/texting the whole time?”

Course Description & Objectives

  • Official course description: On Smith College Course Search.
  • Unofficial course description: A course on the theoretical underpinnings of machine learning. Fundamental concepts of machine learning, such as crossvalidation and the bias-variance tradeoff, will be viewed through both statistical and mathematical lenses. As much of the coursework will center around participation in Kaggle competitions, there will be greater emphasis on supervised learning techniques including regression, smoothing methods, classification, and regularization/shrinkage methods. To this end, there will be a large computational component to the course, in particular the use of tools for data visualization, data wrangling, and data modeling.

Topic Schedule and Readings

We will cover the topics below; the schedule can be found on the main page of this course webpage. We will draw from An Introduction to Statistical Learning with Applications in R. While I have not explicitly assigned readings, please use the book as a resource.

Policies

  • Bring your laptop, a set of headphones, colored pens/pencils, and your paper notebook to every lecture.
  • You are expected to stay until the end of lecture. If you need to leave before the end of lecture, please confirm with me first.
  • Attendance will not be explicitly taken and occasional absenses are excused. However, extended absenses should be mentioned to me.
  • However, you are responsible for asking your peers for what you missed. For example, makeup lectures will not be held during office hours.

Evaluation

Engagement 10%

It is difficult to explicit codify what constitutes “an engaged student,” so instead I present the following rough principle I will follow: you’ll only get out of this class as much as you put in. That being said, here are multiple pathways for you to stay engaged in this class:

  • Asking and answering questions in class.
  • Coming to office hours.
  • Posting questions on Slack. Even better, responding to questions on Slack.
  • Your peer evaluations for your group work.

Midterms 35%

  • Two midterms during the semester
  • Lower score weighted 15% and higher score weighted 20%.

Problem Sets 55%

Policies

  1. Collaboration: While I encourage you to work with your peers for problem sets and labs, you must submit your own answers and not simple rewordings of another’s work. Furthermore, all collaborations must be explicitly acknowledged in your submissions.
  2. Honor Code: All your work must follow the Smith College Academic Honor Code Statement; in particular all external sources must be cited in your submissions.
  3. Mini-projects and DataCamp:
    • No extensions will be granted without a dean’s note.
  4. Midterms/quizzes:
    • No make-up quizzes or midterms will be allowed without a dean’s note.
    • Timestamps for all midterms will be strictly enforced. Honor board cases will be submitted wherever called for.
  5. Grading: I reserve the right to not discuss any grading issues in class and instead direct you to office hours.

Accommodations

Smith is committed to providing support services and reasonable accommodations to all students with disabilities. To request an accommodation, please register with the Disability Services Office at the beginning of the semester. To do so, call 413.585.2071 to arrange an appointment with Laura Rauscher, Director of Disability Services.

Code of Conduct

As the instructor and assistants for this course, we are committed to making participation in this course a harassment-free experience for everyone, regardless of level of experience, gender, gender identity and expression, sexual orientation, disability, personal appearance, body size, race, ethnicity, age, or religion. Examples of unacceptable behavior by participants in this course include the use of sexual language or imagery, derogatory comments or personal attacks, trolling, public or private harassment, insults, or other unprofessional conduct.

As the instructor and assistants we have the right and responsibility to point out and stop behavior that is not aligned to this Code of Conduct. Participants who do not follow the Code of Conduct may be reprimanded for such behavior. Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the instructor.

All students, the instructor, the lab instructor, and all assistants are expected to adhere to this Code of Conduct in all settings for this course: lectures, labs, office hours, tutoring hours, and over Slack.

This Code of Conduct is adapted from the Contributor Covenant, version 1.0.0, available here.