Course Info

Welcome to CPSC 66.

This course will introduce algorithms and frameworks that train computers to learn from data in order to better complete specific tasks. The first part of the course will focus on the task of making predictions (supervised learning). The course will then cover other areas of the field including structured learning, unsupervised learning, and semi-supervised learning, among others. The course will also develop general machine learning methodologies; frameworks for analyzing and validating algorithms and theoretical foundations.

Please be aware that many elements on the course website will change throughout the semester, including the course schedule.

It is your responsibility to review the course website periodically for updates.

We value any and all student feedback. Please be constructive in any comments so that we can adjust the course as best possible. This semester, we are using EdSTEM to manage course discussions and announcements.

Meeting Times:

Section Days Time Room Instructor



11:20 AM - 12:35 PM

SCI 158

Xiaodong Qu

Lab Day Time Room Instructor



1:05 PM - 2:35 PM

SCI 240

Xiaodong Qu



2:45 PM - 4:15 PM

SCI 240

Xiaodong Qu

Support Staff & Office Hours

Name Office Hours Location

Xiaodong Qu

Thursdays, 1:30 PM - 4:30 PM (and by appt)

SCI 252, or Zoom

Course Goals

By the end of the course, we hope that you will have developed the following skills:

  • several machine learning frameworks, including supervised learning, unsupervised learning, and hybrid approaches

  • various algorithms for the frameworks we explore, including the variation in data representation

  • how to choose and apply an appropriate framework and algorithm for a new problem

  • practical considerations for data, including data preprocessing, feature engineering, and resource constraints

  • the core concept of generalization, and the associated theoretical tools for inspecting both our data and models

  • theoretical and empirical evaluation of performance

Inclusion Statement

Diversity, inclusion, and a mutual sense of belonging are all core values of this course. All participants in this course must be treated with respect by other members of the Swarthmore CS community. We must all strive, students and faculty both, to never make anyone feel unwelcome or unsafe in any way. Violations of these principles are viewed as unacceptable, and we take them very seriously. If you ever feel discriminated against or otherwise excluded, no matter how minor the offense, we encourage you to reach out to Xiaodong, or one of the college deans.



Aug 30


Course Introduction

Notes 1
Lab 1

Sep 01


Sep 06


Supervised Learning

  • The Concept
  • Algorithms
  • Examples
  • Related Research Questions

Notes 2
Lab 2

Sep 08

Drop/Add Ends (Sep 09)


Sep 13


Classification and Regression

  • Linear Discriminant Analysis (LDA)
  • Support Vector Machine (SVM)
  • Nearest Neighbors
  • Decision Trees
  • Ensemble methods
  • Related Research Questions

Notes 3
Lab 3

Sep 15


Sep 20


Unsupervised Learning

  • The Concept
  • Algorithms
  • Examples
  • Related Research Questions

Notes 4
Lab 4

Sep 22


Sep 27



  • K-means
  • Hierarchical Clustering
  • Related Research Questions

Notes 5
Lab 5

Sep 29


Oct 04


ML Research

  • Literature Review
  • ML example papers
  • Your understanding of ML
  • ML Research Design
  • Your ML research questions

Notes 6
Lab 6

Oct 06

Mid-Term Due 23:59 EST


Oct 11

Fall Break

Oct 13


Oct 18


Deep Learning

  • The Concept
  • Algorithms
  • Related Research Questions
  • Best Practices
  • Resources
  • Example Papers
  • Your Research paper

Notes 7
Lab 7

Oct 20


Oct 25



  • The Concept
  • Algorithms
  • Related Research Questions
  • Your Research paper

Notes 8
Lab 8

Oct 27


Nov 01



  • The Concept
  • Algorithms
  • Related Research Questions
  • Your Research paper

Notes 9
Lab 9

Nov 03


Nov 08


First draft of your paper

  • Research problems
  • Literature review
  • Methods
  • Results
  • Discussion
  • Conclusion

Notes 10
Lab 10

Nov 10


Nov 15


Peer Review

  • Reviewer guide
  • Innovation
  • Connection
  • Impact
  • Clarity
  • Code
  • Overleaf and LaTeX

Notes 11
Lab 11

Nov 17


Nov 22


Reinforcement Learning

  • The Concept
  • Algorithms
  • Examples
  • Related Research Questions

Notes 12
Lab 12

Nov 24

Thanksgiving Break


Nov 29


Second draft and peer review

  • Research problems
  • Literature review
  • Methods
  • Results
  • Discussion
  • Conclusion

Notes 13
Lab 13

Dec 01


Dec 06



  • The Concept
  • Algorithms
  • Examples
  • Related Research Questions

Notes 14
Lab 14


Dec 12

Final Project Due 23:59 EST

Grading Policies

Grades will be weighted as follows:


Lab assignments


Midterm Project


Final Project


Class Participation

Lab Policy

This course features weekly lab assignments which are the largest component of your course grade. Lab attendance is required by all students. You must attend the lab session for which you are enrolled.

Lab assignments will typically be released on Wednesday and will be due by midnight on the following Tuesday. You are strongly encouraged to start early and to attend the office hours for possible questions.

You will submit your assignments electronically using emails. You may submit your assignment multiple times, and a history of previous submissions will be saved. You are encouraged to submit your work regularly.

Late Policy

Labs will typically be due Tuesdays before midnight. Late submissions will not be accepted. Even if you do not fully complete a lab assignment you should submit what you have done to receive partial credit.

If you feel that you need an extension on an assignment or that you are unable to attend class for two or more meetings due to a medical condition (e.g., extended illness, concussion, hospitalization) or other emergency, you must contact the dean’s office and your instructors. Faculty will coordinate with the deans to determine and provide the appropriate accommodations. Note that for illnesses, the College’s medical excuse policy states that you must be seen and diagnosed by the Worth Health Center if you would like them to contact your class dean with corroborating medical information.

Academic Integrity

Academic honesty is required in all your work. Under no circumstances may you hand in work done with or by someone else under your own name. Discussing ideas and approaches to problems with others on a general level is encouraged, but you should never share your solutions with anyone else nor allow others to share solutions with you. You may not examine solutions belonging to someone else, nor may you let anyone else look at or make a copy of your solutions. This includes, but is not limited to, obtaining solutions from students who previously took the course or solutions that can be found online. You may not share information about your solution in such a manner that a student could reconstruct your solution in a meaningful way (such as by dictation, providing a detailed outline, or discussing specific aspects of the solution). You may not share your solutions even after the due date of the assignment.

In your solutions, you are permitted to include material which was distributed in class, material which is found in the course textbook, and material developed by or with an assigned partner. In these cases, you should always include detailed comments indicating on which parts of the assignment you received help and what your sources were.

When working on quizzes, exams, or similar assessments, you are not permitted to communicate with anyone about the exam during the entire examination period (even if you have already submitted your work). You are not permitted to use any resources to complete the exam other than those explicitly permitted by course policy. (For instance, you may not look at the course website during the exam unless explicitly permitted by the instructor when the exam is distributed.)

Failure to abide by these rules constitutes academic dishonesty and will lead to a hearing of the College Judiciary Committee. According to the Faculty Handbook:

Because plagiarism is considered to be so serious a transgression, it is the opinion of the faculty that for the first offense, failure in the course and, as appropriate, suspension for a semester or deprivation of the degree in that year is suitable; for a second offense, the penalty should normally be expulsion.

This policy applies to all course work, including but not limited to code, written solutions (e.g. proofs, analyses, reports, etc.), exams, and so on. This is not meant to be an enumeration of all possible violations; students are responsible for seeking clarification if there is any doubt about the level of permissible communication.

The general ethos of this policy is that actions which shortcut the learning process are forbidden while actions which promote learning are encouraged. Studying lecture materials together, for example, provides an additional avenue for learning and is encouraged. Using a classmate’s solution, however, is prohibited because it avoids the learning process entirely. If you have any questions about what is or is not permissible, please contact your instructor.

Academic Accommodations

If you believe you need accommodations for a disability or a chronic medical condition, please contact Student Disability Services (via email at to arrange an appointment to discuss your needs. As appropriate, the office will issue students with documented disabilities or medical conditions a formal Accommodations Letter. Since accommodations require early planning and are not retroactive, please contact Student Disability Services as soon as possible.

For details about the accommodations process, visit the Student Disability Services website.

To receive an accommodation for a course activity, you must have an official accommodation letter from the Office of Student Disability Services and you need to meet with course staff to work out the details of your accommodation at least two weeks prior to the activity.

You are also welcome to contact any of the course staff privately to discuss your academic needs. However, all disability-related accommodations must be arranged, in advance, through Student Disability Services.

Programming Style

Programming is not a dry mechanical process, but a form of art. Well written code has an aesthetic appeal while poor form can make other programmers and instructors cringe. Programming assignments will be graded based on style and correctness. Good programming practices usually include many of the following principles:

  • A comment at the top of the program that includes:

    • Program authors

    • Date or Dates

    • A brief description of what the program does

  • Concise comments that summarize major sections of your code

  • Meaningful variable and function names

  • Function comments that include:

    1. description of what function does

    2. description of input value(s) (parameter values)

    3. description of return value

  • Well organized code

  • White space or comments to improve legibility

  • Avoidance of large blocks of copy-pasted code


This semester, we are using EdSTEM to manage course discussions and announcements.