Syllabus

CSCI 200B Interim 2026 Special Topics: Machine Learning Morning Sessions: 10:40-12:40 in RNS 203 Afternoon Sessions: 1-3 in RNS 203

General Information

Mission: This is a welcoming, inclusive, encouraging, and failure-tolerant class.
Instructor: Kim Mandery
E-mail: mander1@stolaf.edu
Office: RMS 407

Textbook: Introduction to Statistical Learning with Python (ISL-P). This book is free and available online, and is also hosted on this site. You can download your own pdf or access via your browser here

Course Description

It has become increasingly common to use machine learning algorithms to analyze data, draw conclusions, and build models, without direct human instruction. These algorithms have been used in a wide variety of applications, including Netflix recommendations, predicting healthcare outcomes, criminal justice, and many more. In this course, we’ll explore several common machine learning algorithms, learning how they work, and applying them to real datasets. We will cover the strengths and limitations of machine learning algorithms. We will also explore real-world applications of machine learning, and discuss the ethical and societal consequences of the use of these algorithms.

Course Goals

Through the lens of machine learning, we will:

  • develop and interpret applications of algorithms to domain use-cases
  • develop working software that satisfies coding best practices
  • work effectively, both individually and in teams
  • communicate information effectively to both technical and non-technical audiences

Course Schedule

The schedule for this course is available by navigating to Course Information > Schedule on the navigation window to the left. This page is likely to be updated throughout the semester, and will be announced in class if it is.

Active Learning and Engagement

I expect you to take an active role in class each day, participating positively, wholeheartedly, and respectfully in class/group discussions and other activities. You may need sick/mental health days. You are responsible for learning the material on the day you missed. If you are experiencing issues causing you to miss classes often, please speak with me as this may be evident of a possible failing grade. You can also visit your class dean as they are a great resource.

Course Breakdown

Readings

(optional): Each module has selected readings related to the content covered in class, consisting of textbook passages from Introduction to Statistical Learning (ISL-P), as well as articles on ethics and/or domain use-cases. In a typical semester-long course, these readings would be required. In our four-week course, these readings are optional. If you find that you have questions on a particular topic, check out the readings to find related information.

Article Reviews

(5pt * 3 → 15 pt): In place of readings, you will be required to find three (3) articles related to machine learning, and complete a review on each. The article reviews are due before the final exam, though it is recommended to finish them as early as possible. Read more about article reviews here.

Homework Assignments

(5pt * 8 → 40 pt): We will have a homework assignment for each module that we cover, which will be due at the start of class of the following class. These homework assignments consist of exercises to complete by hand (rather than programming), to reinforce your understanding of the concepts. Collaboration on homework is encouraged, though you are responsible for understanding the computations as you will be assessed on similar questions during your quizzes.
Late work: Due to the short turn-around on turning homework in and taking the module quiz, no late work will be accepted.

Lab Assignments

(5pt * 9 → 45 pt): We will have labs associated with every module and you are encouraged to work on labs with one other person. Only one partner needs to submit the lab for the group. Labs are due before we take the corresponding quiz. These assignments consist of programming exercises to practice applying the techniques we cover. Typically, these assignments will be completed by filling in missing code into Jupyter notebooks using Google Colab, and then submitting your completed notebook. Each exercise will be graded all-or-nothing, with no partial credit. Late work: Due to the short turn-around on turning labs in and taking the module quiz, no late work will be accepted.

Semester Project

(80 pts): Over the entirty of this course, you will complete a personal project where you apply the concepts covered in the course to a topic of your choice. You can find more in-depth information regarding the project on the project page

The project will be divided into the following components:

  • Progress Checks (4pt * 9 → 36 pt): There are nine progress checks during the semester, each worth 4 points (see the schedule). To receive full credit for a progress check, you must check in with your instructor before leaving class on the day of the check.
  • Final Submission (30 pts): The final submission evaluates the overall quality of your project and presentation, based on the materials submitted prior to the final exam. Submissions are due the night before finals. A detailed grading rubric will be available on the project page.
  • Small group presentations (9 pts): During presentations, you will participate in three rounds of small-group presentations, presenting one-on-one with another project. Grading is based on the quality of your peer review during these rounds. The peer review form will be available on the project page.
  • Reflection (5 pts): Complete the reflection form up on the project page. This form asks you what went well, what could be improved upon, ect.

Late Work: No late work will be accepted.

Standards based Quizzes

(8pt * 9 → 72 pt): Each module will have a quiz that will be assessed using standards-based grading. Each quiz will contain two (2) questions called standards. There will be a study guide for each quiz that indicates which standards are being assessed. The full list of standards is in the section ML Core Standards found on this page.

You will be given three attempts to pass each standard. The first attempt will be during the module quiz as indicated in the course schedule. The second attempt will be during the retake day associated with each module (see schedule). All final attempts for standards will take place during our final exam time after presentations.

The standards are graded on an 8-point scale and binned into four categories:

  • P proficient (8 pt)
  • R small revision needed
  • N not satisfactory (4 pt)
  • I incomplete (0 pt)

Revisions must be handed in before your next quiz. Standards with an R have a fully correct solution handed in will be bumped up to a P. Otherwise it will get bumped down to an N.

Any student with N or I for three or more standards at the end of the course will be ineligible for a final grade greater than a B- in the course.

Extra Credit

Some projects have additional questions you can answer for extra credit.

Final Exam

The final exam is during the assigned period:

Friday, Jan 30 from 10:40 - 3:00 P.M.

Any final attempt on standards will take place during the second half of our final time. If you successfully passed each standard, you do not have to attend the second half of our final exam.

Final Grade Scale

97 ≤ A+ ≤ 100 87 ≤ B+ ≤ 90 77 ≤ C+ ≤ 80 67 ≤ D+ ≤ 70
93 ≤ A ≤ 97 83 ≤ B ≤ 87 73 ≤ C ≤ 77 63 ≤ D ≤ 67
90 ≤ A- ≤ 93 80 ≤ B- ≤ 83 70 ≤ C- ≤ 73 60 ≤ D- ≤ 63

Explanations of each letter grade range can be found at https://catalog.stolaf.edu/academic-regulations-procedures/grades/

Syllabus Changes

All course information provided in this syllabus is subject to change and/or elimination. Changes will only be made when the instructor feels they are in the best interest of the class. It is the responsibility of every student to attend class, check e-mail, and communicate with the instructor to be informed and understand these changes.

Most importantly HAVE FUN – I am really looking forward to this semester!

ML Core Standards

Adopted from ACM standards, the standards are grouped into nine categories that replicate a typical ML workflow.

Workflow

  • definition and examples of a broad variety of machine learning tasks:
    • supervised learning (classification, regression)
    • unsupervised learning (clustering);
  • ability to identify, construct, and critique ML workflows

Standards include: FLOW, FLOW2, FLOW-LIN, ALG-CLS

EDA

  • understanding of statistical measures, distributions, and tests
  • exploration of data to guide workflow design and model selection

Standards include: EDA-STAT

Data

  • data preprocessing (importance and pitfalls)
  • handling missing values (imputing, flag-as-missing, implications)
  • encoding categorical variables and real-valued data
  • normalization and standardization.

Standards include: DATA-PREP

Selection

  • importance of understanding what your model is actually doing, where its pitfalls or shortcomings are, and the implications of its decisions
  • no free lunch (no one learner can solve all problems)
  • representational design decisions have consequences
  • sources of error and undecidability in machine learning

Standards include: FLOW-LIN, MOD-SEL

Algorithms

  • fundamentals of understanding how common ML algorithms work (including but not limited to):
    • clustering
    • tree-based
    • regression-based
    • ensemble
    • neural nets
    • deep learning
  • ability to explain algorithms and their associated terms to a non-technical audience (including but not limited to):
    • objective function
    • gradient descent
    • regularization
    • entropy

Standards: ALG-CLS, ALG-TREE, ALG-LIN, ALG-ESB, ALG-NN, ALG-DL

Training

  • separation of train, validation, and test sets
  • tuning the parameters of a machine learning model with a validation set
  • cross validation
  • overfitting problem / controlling solution complexity (regularization, pruning–intuition only)
  • the bias(underfitting) - variance (overfitting) tradeoff Standards: TRAIN, TRAIN-TREE

Evaluation

performance metrics for classifiers; estimation of test performance on held-out data; other metrics for classification (e.g., error, precision, recall); performance metrics for regressors; confusion matrix;

Standards: EVAL, EVAL-CLS

Ethics

  • focus on real data, scenarios, and case studies
  • bias present in datasets, algorithms, evaluation
  • privacy
  • fairness
  • ethical matrix

Policies and Resources

The mission of this course is to make everyone feel welcome, not only in our classroom, but also in the field of computer science. If there is any reason you do not feel welcome in this class, please talk to me. If anything is said to you that makes you feel uncomfortable, please talk to me. If there is any life event that is interfering with your learning in this class, please talk to me. I may not be able to solve all your problems, but I can direct you to the right resources on campus.

Inclusivity and Community

In keeping with St. Olaf College’s mission statement, this class strives to be an inclusive and antiracist learning community, respecting and promoting those of differing backgrounds and beliefs. As a community, we will be to be respectful to all citizens in this class, regardless of race, ethnicity, religion, gender, or sexual orientation. This course affirms people of all gender expressions and gender identities. If you prefer to be called a different name or pronoun than what is on the class roster, please let me know.

Illness and Community Standards

Out of respect for our learning community, if you are experiencing any symptoms of an illness, do not come to class or my office. Please contact me before class time to let me know of your absence.

Cell Phone Policy and Classroom Atmosphere

You are expected to contribute to a positive classroom atmosphere, which includes arriving on time, not being disruptive, being respectful, actively involving yourself in class, and silencing and putting away cell phones. There may come a time where we use technology in class – during this time you can use a laptop, tablet, or your phone if you do not have the other options available to you. At any other times, however, I do not want to see your phone out. I’ll ask you to put them away if they show up.

Mental and Physical Health

I greatly value your experience in this class, and it is my duty to facilitate a safe, caring, and productive learning environment. I recognize that you may experience a range of emotional, physical, and/or psychological issues, both in and out of the classroom, that may distract you from your learning. If you are experiencing such issues, please do not hesitate to see me– I am here to listen. We can also discuss what further resources might be available to you.

Academic Accommodations

I am committed to supporting the learning of all students in my class. If you have already registered with Disability and Access (DAC) and have your letter of accommodations, please meet with me as soon as possible to discuss, plan, and implement your accommodations in the course. If you have or think you have a disability (learning, sensory, physical, chronic health, mental health or attentional), please contact Disability and Access staff at 507-786-3288 or by visiting https://wp.stolaf.edu/academic-support/dac.

Multilingual Students

I am committed to making course content accessible to all students. If English is not your first language and this causes you concern about the course, please speak with me. Students who would like extra support with writing or speaking in English can also contact the language specialist in CAAS.

Managing stress, anxiety, and other issues

I greatly value your experience in this class, and it is my duty to facilitate a safe, caring, and productive learning environment. I recognize that you may experience a range of emotional, physical, and/or psychological issues, both in and out of the classroom, that may distract you from your learning. If you are experiencing such issues, please do not hesitate to come see me – I am here to listen. We can also discuss what further resources might be available to you.

Plagiarism & Academic Integrity

Plagiarism, the unacknowledged appropriation of another person’s words or ideas, is a serious academic offense. It is imperative that you hand in work that is your own, and that cites or gives credit to others whenever you draw from their work. This includes citing prompts when using genAI tools or an internet search. Please see St.Olaf’s statements on academic integrity and plagiarism at: https://wp.stolaf.edu/thebook/academic/integrity/. See also the description of St.Olaf’s honor system at: https://wp.stolaf.edu/honorcouncil/.

Communication Expectations:

  • Instructor/Student Expectations: I check my email frequently, and attempt to respond to questions within 24 hours. It should not be expected that I check my email after 9 PM, on Saturdays, or regularly during breaks.
  • E-mail Etiquette: If you have multiple questions, fewer emails with more information per email is preferred. Please include the following when sending an email: course number with section number, a description of your question(s) within the subject line, a greeting and your instructors preferred name (mine is Kim), and a sign-off using your preferred name (this is what you want others to refer to you in class by). Add screenshots of your work for clarity.

The St. Olaf Honor System

The St. Olaf Honor System has been in place at St. Olaf College since 1911. All tests, quizzes, and examinations of any kind are taken under the St. Olaf Honor System. Each student is responsible for adhering to all principles of the Honor System regardless of the individual circumstances associated with their assessment environment.