Lectures: Mondays, & Wednesdays 10-11:20 in THO 125

Office hours: TBD (may change by +/-30 min -- )

TA: Jess Phillipps

Course web page: http://www.stat.washington.edu/mmp/courses/527/spring23 will be used to post lecture notes before and after lecture, and homeworks. The resources page is useful for addional reading. Other materials may be posted on Canvas. Everything posted here that is directly relevant to the learning objectives will be linked to from Canvas, hence you won't need to visit the web site once you are registered.

Class mailing list: multi_biost527a_sp23 at UW will be used sparingly by me mainly for announcements, but is open for posting by everyone on the class mailing list.

Learning Goals

• Understanding the foundational properties of NP models
• Ability to understand these properties when expressed in mathematical/quantitative terms
• Ability to implement and use NP models on real data sets
• Ability to interpret NP models, and to validate the statistical inferences based on them
• Ability to apply the knowledge gained in a data analysis project.
• Ability to communicate about the properties of NP models, their behavior on data and their implementation

Syllabus

• Part I. Basics
• Nearest neighbor prediction and density estimation
• Kernel prediction and density estimation
• Non-parametric density-based clustering
• [Crossvalidation and boostrap]
• Part II. Intermediate
• Double descent
• Kernel Machines
• [Random Forests]
• Manifold learning
• Part III. Advanced
• Gaussian processes (GPs)
• Neural networks as GPs and the Neuro-Tangent kernel
• NP-Bayes, Dirichlet processes for non-parametric clustering
• [shape constrained estimation, computational issues with big data, conformal prediction]
• 
  Some advanced topics and topics in [] may be skipped, depending on time constraints and interest.

Grading:The grade is based on homework + quizzes (60-70%), project (15-25%), class participation (5-10%). These percentages are approximative, and may be refined later in the quarter.
The grading policies described here apply to students in good standing; students who engage in misconduct will be reported to the office of Community Standards & Student Conduct, their grades, too, may be handled differently.

• Assignments: Typically assignments will be posted on the web on Tuesdays (may be switched to Thursday) and will be due the next Tuesday at the beginnining of class. More generally, homework will be due one week after it is posted, and we will post a homework approximately every week. Graded homeworks and solutions will be available about a week after the homework due date.
  Submit each homework as a single .pdf file through Canvas.
  The assignments will consist of (1) programming assignments (typically, to implement a version or a special case of an algorithm presented in the lecture) to be done in the programming language of your choice and (2) problems or other questions, including proofs. The programming assignments will be split into two separate parts:
  • Output and results are the only part of the programming assignment that is graded; they must be included in the single .pdf file that you submit.
  • The code, which is submitted electronically, and separately from the homework pdf. You can submit the code in one or more files, a zip archive, or any usual and convenient format. Code is typically not graded.

    With the exception of generic libraries (like plotting, matrix functions) you must write your own code. In particular, you are not to use matlab, R or python code for machine learning that is available on the web or with the textbook(s).

  Late homeworks will be accepted in exceptional circumstances. Please let us know in advance if you think you will be late.
  Teamwork: Each class participant submits her/his homework individually. Unless explicitly allowed to do so, you are required to write your own code. Discussing homework questions is acceptable as long as hints or solutions are not asked for or given. For example, a discussion to clarify what a question requires , on the discussion board or elsewhere, is acceptable. Asking what to do (e.g. "what do I plug into this formula")? is not acceptable; it is also not acceptable to answer such questions.
• Quizzes will be announced. A quiz will take 10-15 minutes at the beginning of class; . We cannot give the quizzes on-line, but we are considering ways to compensate if a student misses a quiz. We are also planning to allow a good response to quizzes to replace part of a homework, and will announce these changes in advance of the respective quiz and homework.
• Participation What counts as participation? During lectures: Ask questions in class (chat OK if class on-line). Reply to the questions I pose during lectures.
  On the discussion board: There will be occasional questions marked as counting for participation asked by instructor, or TA, or other students. Note: discussion about the homework typically does not qualify for participation, but there are exceptions. For example, if you find an error in the homework and are the first to point it out to us, congratulations! we consider that participation.
  How much participation is enough? Once a week on average, either in class or on discussion board, is plenty.
  Note that class attendance by itself is not graded as participation, and is not required.
• Project (tentative) Each project will have the same training set as starting point. You will perform a prediction task on this data set. You will submit a report describing what you did, submit your code, and test your trained predictor(s) on a test set with hidden labels. Evaluation of the project is based partly on the report and partly on the test set results. Time permitting, we will have short presentations/Q\&A during the last lecture time. More details about the project will be announced around November 1.

Prerequisites

• (STAT 502 and STAT 504) or (BIOST 514 and BIOST 515)
• In more detail:
• Fundamentals of statistics, such as regression, classification, density estimation
• Calculus and linear algebra: partial derivatives, gradient, Hessian, the chain rule, vectors and matrices, matrix multiplications, eigenvalues and eigenvectors, positive definite matrices
• [Algorithms and data structure at a basic level (arrays, lists, sets, O( ) notation) are helpful]
• Medium (beyond beginner) ability with a computer programming language (like C, C++, Java or Matlab, Splus, R) at the level of STAT 534

Staying safe from infectious diseases during the lectures and office hours will follow UW policy. Wearing a mask and distancing when you believe you might be harboring an infection to keep everyone in the class safe will be greatly appreciated.

Religious accomodation 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 fororganized religious activities. The UW’s policy, including more information about how to requestan accommodation, is available at Religious Accommodations Policy. Accommodations must berequested within the first two weeks of this course using the Religious Accommodations Request Form.

The UW food pantry A student should never have to make the choice between buying food or textbooks. The UW Food Pantry helps mitigate the social and academic effects of campus food insecurity. They aim to lessen the financial burden of purchasing food by providing students withaccess to food and hygiene products at no-cost. Students can expect to receive 4 to 5 days’ worth ofsupplemental food support when they visit the Pantry. For information including operating hours,location, and additional food support resources visit The UW Food Pantry. They can be found onthe North side of West Campus’ Poplar Hall at the corner of Brooklyn Ave NE and 41st. Notice on Zoom class activities (in case we use zoom) We do not plan to record the office hours, or other class activities thay may take place on zoom, with the exception of lectures. If we decide that the learning goals are better achieved by recording, we will announce it in advance. Any recordings we may make will only be accessible to students enrolled in the course to review materials. These recordings will not be shared with or accessible to the public.