DTSA 5509 Introduction to Machine Learning: Supervised Learning

Instructor Geena Kim  

Course Description 

In this course, you’ll be learning various supervised ML algorithms and prediction tasks applied to different data. You’ll learn when to use which model and why, and how to improve the model performances. We will cover models such as linear and logistic regression, KNN, Decision trees and assembling methods such as Random Forest and Boosting, kernel methods such as SVM. 

MS-DS Program Learning Outcomes  

Successful completion of this course demonstrate your achievement of the following learning outcomes for the MS-DS program: 

• Correctly perform exploratory data analyses in order to assist with the generation of scientific hypotheses. 

• Construct an appropriate statistical model in order to answer important scientific or business-related questions. 

• Assess the validity of a statistical model when applied to a particular dataset. 

• Correctly apply the data science skills above to a specific domain area (e.g., business, climate science). 

• Clearly communicate the results of a data science analysis to a non-technical audience. 

• Structure effective meetings and projects. 

• Use peer feedback, self-reflection and video analysis to improve collaboration skills. 

• Create reproducible statistical workflows. 

• Act ethically in the role of professional data scientist.  

Drops, Tuition Refunds, and Withdrawals 

Because the MS-DS has flexible course start dates, all drops, tuition refunds, withdrawals and grades are handled at the individual course level. It is the student’s responsibility to monitor these deadlines. Coursera and CU Boulder are not responsible for notifying the students of these deadlines. For approximate session timelines, access the Boulder MS-DS Onboarding Course via the MS-DS degree homepage. To drop or withdraw from a course please complete the appropriate form on the CU Boulder Office of the Registrar website.  

Drops and Refunds 

Each student has 14 days from a class start date or their enrollment date (whichever is later) to request a drop for 100% tuition refund. Students are only eligible to drop a course if they have not accessed the restricted content (timed proctored exam) or received a grade.  When a course is dropped under these conditions, it will not appear on the student’s record. 

Program Policies 

Suspected Violations of AI Tool Usage Policy 

If program staff suspects you may have used AI tools to complete assignments in ways not explicitly authorized or suspect other violations of the honor code, they will contact you via email. Be sure to respond promptly to any related communication so your perspective is included in the case review. Failure to respond timely will not prevent the completion of a case review.  

In suspected cases of unauthorized AI tool usage, the program may: 

• Request the documentation noted above (see AI Usage Documentation Guidelines) or other supplementary materials 

• Issue a warning 

• Assign a 0–50% grade for the question 

• Assign a 0–50% grade for the assignment 

• Assign an F grade for the course  

• Reference prior violations 

• Remove access to the course, related materials, and tools 

• Contact the Office of Student Conduct & Conflict Resolution to report suspected Honor Code violations 

Turnitin and similar AI detection tools may be used in these courses for initial detection of possible honor code violations. All suspected violations will be reviewed by a human. AI tools alone will not be used to determine if an assignment is plagiarized, and results from these tools will not be used alone as evidence to penalize students. 

University Policies 

Accommodation for Disabilities 

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. 

Classroom Behavior 

Students and faculty each have responsibility for maintaining an appropriate learning environment. Those who fail 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. Class rosters are provided to the instructor with the student's legal name. We will gladly honor your request to address you by an alternate name or gender pronoun. Please advise us of this preference early in the semester so that we may make appropriate changes to my records. For more information, see the policies on classroom behavior and the Student Code of Conduct. 

Honor Code 

The University of Colorado Boulder takes issues of academic dishonesty extremely seriously.