Machine learning explores the study and construction of algorithms that can learn from historical data and make inferences about future outcomes. This study is a marriage of algorithms, computation, and statistics so this class will be have healthy doses of each. The goals of this course are to provide a thorough grounding in the fundamental methodologies and algorithms of machine learning.
Prerequisites: Students entering the class should be comfortable with programming and should have a pre-existing working knowledge of linear algebra (MATH 308 or equivalent), vector calculus (MATH 126 or equivalent), probability and statistics (CSE 312/STAT390 or equivalent), and algorithms. For a brief refresher, we recommend that you consult the linear algebra and statistics/probability reference materials on the Textbooks page.
Grading: Your grade will be based exclusively on 5 homework assignments: HW0 (10%), HW1 (20%), HW2 (20%), HW3 (20%), HW4 (30%). There are no exams or credit given in any way other than the homeworks (e.g., no credit given for attending lecture or section). Curving will be based on an affine transformation of the scores up to the discretion of the instructor and will not be publically posted.
We take academic integrity very seriously. Behaving with integrity is part of our responsibility to our shared learning community. Please read the UW Student Conduct Code: Academic Misconduct for more information. If you’re uncertain about if something is academic misconduct, please ask the Instructor or TAs. We are happy to discuss any questions you may have.
This course welcomes all students of all backgrounds. The computer science and computer engineering industries have significant lack of diversity. This is due to a lack of sufficient past efforts by the field toward even greater diversity, equity, and inclusion. The Allen School seeks to create a more diverse, inclusive, and equitable environment for our community and our field. You should expect and demand to be treated by your classmates and the course staff with respect. If any incident occurs that challenges this commitment to a supportive, diverse, inclusive, and equitable environment, please let the instructor know so the issue can be addressed.
University policy prohibits all forms of sexual harassment. If you feel you have been a victim of sexual harassment or if you feel you have been discriminated against, you may speak with your instructor, teaching assistant, the chair of the department, or you can file a complaint with the UW Ombudsman's Office for Sexual Harassment. Their office is located at 339 HUB, (206) 543-6028. There is a second office, the University Complaint Investigation and Resolution Office (UCIRO), who also investigate complaints. The UCIRO is located at 22 Gerberding Hall. Please see additional resources at:
http://www.washington.edu/about/ombudsman/role.html and http://f2.washington.edu/treasury/riskmgmt/UCIRO .
Embedded in the core values of the University of Washington is a commitment to ensuring access to a quality higher education experience for a diverse student population. Disability Resources for Students (DRS) recognizes disability as an aspect of diversity that is integral to society and to our campus community. DRS serves as a partner in fostering an inclusive and equitable environment for all University of Washington students. The DRS office is in 011 Mary Gates Hall. Please see the UW resources at: http://depts.washington.edu/uwdrs/current-students/accommodations/.
Washington state law requires that UW develop a policy for accommodation of student absences or significant hardship due to reasons of faith or conscience, or for organized religious activities. The UW’s policy, including more information about how to request an accommodation, is available at Religious Accommodations Policy:
Accommodations must be requested within the first two weeks of this course using the Religious Accommodations Request form: (https://registrar.washington.edu/students/religious-accommodations-request/).