CSCI 5622: Machine Learning

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Instructor Information

Name: Esther Rolf

Office Location: ECES 122

My office hours are 11am-12pm on Mondays, and by appointment (no office hours Mon 1/20)

You can reach me at esther.rolf@colorado.edu

You can read more about my academic work and research on my.

Course Information

Course prerequisites: CSCI 2820 or APPM 3310 or MATH 2130 or CSCI 3022 or APPM 4570 or APPM 3570 or STAT 4250 or MATH 3510 or CVEN 3227 or ECEN 3810 or ECON 3818 or MCEN 4120 (all min grade B). Restricted to Graduate Students Only.

Course description and purpose: This course trains students to build computer systems that learn from experience. It emphasizes practical and theoretical understanding of the most widely used algorithms (neural networks, decision trees, support vector machines) and covers connections to data mining and statistical modeling. It further trains students to address ethical considerations in their design and implementation of machine learning systems. A strong foundation in probability, statistics, multivariate calculus, and linear algebra is highly recommended.

Learning Outcomes

This course is structured to achieve the following learning outcomes:

  • Obtain a good understanding of the core issues and challenges in machine learning, encompassing aspects such as data handling, model selection, model complexity.
  • Develop insight into the advantages and limitations of popular machine learning methodologies.
  • Explore inherent mathematical relationships within supervised and unsupervised algorithms.
  • Design and implement various machine learning algorithms in a range of real-world applications.
  • Explore ethical implications of deploying machine learning algorithms in real-life.

Textbooks and Materials

  • Introduction to Machine Learning (4th Edition), Ethem Alpaydin, MIT Press, 2020, ISBN: 9780262043793,
  • Learning from Data, Yaser S. Abu-Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin, 2012,
  • The Elements of Statistical Learning (2nd Edition), Trevor Hastie, Robert Tibshirani, Jerome Friedman, Springer Series in Statistics, 2017,
  • Additional reading material will be available on CANVAS.

Assignments

Exams (40 points)

There will be two exams in this class. Exam 1 (20 points) will be held on March 3, 2025 during class time. Exam 2 (20 points) will be held on April 30th, 2025 during class time.

Homework (40 points)

There will be a total of 4 homework assignments, comprising mathematical and programming problems relevant to various course components. These assignments will be available on CANVAS. The first 3 homeworks are individual and carry 8 points each. Homework 4 will be done in teams (randomly assigned) and carries 16 points. Late submissions are accepted with a 1-week grace period after the deadline and 1 point penalty (i.e., for an 8 point assignment, 1 out of 8 points will be deducted per assignment). Assignments more than 1 week late will not be graded. Late submissions will not be accepted for homework 4.

Quiz (20 points)

There will be a total of 6 quizzes, each carrying 4 points. The grades will be based on the top 5 quiz scores, and unfortunately, there are no opportunities for quiz makeups.

Regrade Policy

Regrade requests will be accepted only up to 5 business days after grades are released for each assignment/exam. Regrade requests should be sent to the course manager via email. Regrade requests sent after 5 business days after grades have been released will not be reviewed.

Course Calendar (2025)

A detailed version of the course calendar that will be updated throughout the semester with topics and assignment dates can be found here:

  • Week 1: 1/13-1/17
    • Introduction to Machine Learning
  • Week 2 1/20-1/24
    • Linear Algebra Review (Monday is MLK Jr. Day holiday)
  • Week 3, 1/27-1/31
    • K-Nearest Neighbor (basics, hyper-parameter tuning, modified K-NN)
  • Week 4, 2/3-2/7
    • Linear perceptron algorithm, Data pre-processing
  • Week 5, 2/10-2/15
    • Data pre-processing, Linear regression (basics, least squares)
  • Week 6, 2/17-2/21
    • Linear regression (gradient descent) Non-linear regression and regularization
  • Week 7, 2/24-2/28
    • Logistic regression
  • Week 8, 3/3-3/7
    • Exam 1 (in class) 3/3, Neural networks
  • Week 9, 3/10-3/15
    • Deep learning
  • Week 10, 3/17-3/21
    • Deep learning
  • Week 11, 3/24-3/26
    • Spring Break Dimensionality reduction, Quiz 5
  • Week 12, 3/31-4/4
    • Explainable AI, Decision Trees and Random Forests
  • Week 13, 4/7-4/11
    • Decision Trees and Random Forests, Dimensionality Reduction
  • Week 14, 4/14-4/18
    • Unsupervised Learning, Ethics and trustworthiness in machine learning, Quiz 6
  • Week 15, 4/21-4/25
    • Final HW team presentations
  • Week 16, 4/28-4/30
    • Review session, Exam 2 (in class) 4/30

Grading

The grading for the class will be as follows:

  • A Numerical Grade 93 & Above
  • A- Numerical Grade 90-92
  • B+ Numerical Grade 86-89
  • B Numerical Grade 83-85
  • B- Numerical Grade 80-82
  • C+ Numerical Grade 76-79
  • C Numerical Grade 73-75
  • C- Numerical Grade 70-72
  • D+ Numerical Grade 66-69
  • D Numerical Grade 63-65
  • D- Numerical Grade 60-62
  • F Numerical Grade 59 or Below

Using Canvas and Other Technologies

All course material, including slides, readings, and assignments, will be uploaded on CANVAS. Grades for each assignment, quiz, and exam will be available on CANVAS. Course-related discussions will be on Piazza.

Classroom Behavior

Students and faculty are responsible for maintaining an appropriate learning environment in all instructional settings, whether in person, remote, or online. Failure to adhere to such behavioral standards may be subject to discipline. Professional courtesy and sensitivity are especially important with respect to individuals and topics dealing with race, color, national origin, sex, pregnancy, age, disability, creed, religion, sexual orientation, gender identity, gender expression, veteran status, political affiliation, or political philosophy.

For more information, see the classroom behavior policy, the Student Code of Conduct, and the Office of Institutional Equity and Compliance.

Acceptable use of AI in this class

You may use generative AI tools on homework assignments in this course, but their use is limited to the following particular tasks: grammar, language and spelling checks (not for rewriting entire sections of the assignment, generating ideas or outlines). The final work must be student-generated with proper critical evaluation and original analysis or as outlined for each individual assignment. If you use generative AI tools on assignments in this class, document your usage in your report.

Requirements for Infectious Disease

Members of the CU Boulder community and visitors to campus must follow university, department, and building health and safety requirements and all applicable campus policies and public health guidelines to reduce the risk of spreading infectious diseases. If public health conditions require, the university may also invoke related requirements for student conduct and disability accommodation that will apply to this class.

If you feel ill and think you might have COVID-19 or if you have tested positive for COVID-19, please stay home and follow the . If you have been in close contact with someone who has COVID-19 but do not have any symptoms and have not tested positive for COVID-19, you do not need to stay home but should follow the guidance of the CDC for masking and testing.

Accommodation for Disabilities, Temporary Medical Conditions, and Medical Isolation

If you qualify for accommodations because of a disability, please submit your accommodation letter from Disability Services to your faculty member in a timely manner so that your needs can be addressed. Disability Services determines accommodations based on documented disabilities in the academic environment. Information on requesting accommodations is located on the Disability Services website. Contact Disability Services at 303-492-8671 or dsinfo@colorado.edu for further assistance. If you have a temporary medical condition, see Temporary Medical Conditions on the Disability Services website.

If you have a required medical isolation for which you require adjustment, please notify the instructor 24 hours in advance when possible.

Preferred Student Names and Pronouns

CU Boulder recognizes that students' legal information doesn't always align with how they identify. Students may update their preferred names and pronouns via the student portal; those preferred names and pronouns are listed on instructors' class rosters. In the absence of such updates, the name that appears on the class roster is the student's legal name.

Honor Code

All students enrolled in a Î÷¹ÏÊÓƵ course are responsible for knowing and adhering to the Honor Code. Violations of the Honor Code may include but are not limited to: plagiarism (including use of paper writing services or technology [such as essay bots]), cheating, fabrication, lying, bribery, threat, unauthorized access to academic materials, clicker fraud, submitting the same or similar work in more than one course without permission from all course instructors involved, and aiding academic dishonesty.

All incidents of academic misconduct will be reported to Student Conduct & Conflict Resolution: honor@colorado.edu, 303-492-5550. Students found responsible for violating the Honor Code will be assigned resolution outcomes from the Student Conduct & Conflict Resolution as well as be subject to academic sanctions from the faculty member. Visit Honor Code for more information on the academic integrity policy.

Sexual Misconduct, Discrimination, Harassment and/or Related Retaliation

CU Boulder is committed to fostering an inclusive and welcoming learning, working, and living environment. University policy prohibits protected-class discrimination and harassment, sexual misconduct (harassment, exploitation, and assault), intimate partner violence (dating or domestic violence), stalking, and related retaliation by or against members of our community on- and off-campus. These behaviors harm individuals and our community. The Office of Institutional Equity and Compliance (OIEC) addresses these concerns, and individuals who have been subjected to misconduct can contact OIEC at 303-492-2127 or email cureport@colorado.edu. Information about university policies, reporting options, and support resources can be found on the OIEC website.

Please know that faculty and graduate instructors must inform OIEC when they are made aware of incidents related to these policies regardless of when or where something occurred. This is to ensure that individuals impacted receive outreach from OIEC about resolution options and support resources. To learn more about reporting and support for a variety of concerns, visit the Don’t Ignore It page.

Religious Accommodations

Campus policy requires faculty to provide reasonable accommodations for students who, because of religious obligations, have conflicts with scheduled exams, assignments or required attendance. Please communicate the need for a religious accommodation in a timely manner. In this class, please notify the instructor at least 48 hours ahead of time.

See the campus policy regarding religious observancesfor full details.

Mental Health and Wellness

The Î÷¹ÏÊÓƵ is committed to the well-being of all students. If you are struggling with personal stressors, mental health or substance use concerns that are impacting academic or daily life, please contact Counseling and Psychiatric Services (CAPS) located in C4C or call (303) 492-2277, 24/7.

Free and unlimited telehealth is also available through Academic Live Care. The Academic Live Care site also provides information about additional wellness services on campus that are available to students.