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Machine Learning and Big Data PhD Track

The UW Department of Statistics now offers a PhD track in the area of Machine Learning and Big Data. All incoming and current students are eligible to apply. The goal of the PhD track is to prepare students to tackle large data analysis tasks with the most advanced tools in existence today, while building a strong methodological foundation. Students in this track will have a multidisciplinary experience, taking courses across departments and interacting with faculty and graduate students from these departments. A similar PhD track is being offered in Computer Science and Engineering (CSE), and students from both of these tracks will interact significantly in the core courses.

More details about ML @ UW can be found here and here.

For application details, click here.

Program Requirements

  1. Statistics Core: STAT 570, STAT 581, STAT 582
  2. ML/BD Core:
  3. (i) Foundational ML: STAT 535
    (ii) One advanced ML course: STAT 538 or STAT 548
    (iii) One CSE course: CSE 544 (Databases) or CSE 512 (Visualization)
    (iv) One elective:
          * Advanced Statistical Learning (STAT 538)
          * Machine Learning for Big Data (STAT 548)
          * Graphical Models (CSE 515)
          * Visualization (CSE 512)
          * Databases (CSE 544)
          * Convex Optimization (EE 578)
  4. All other statistics PhD requirements hold, except STAT 571 may be used in place of the consulting project course.

Advanced Data Science Transcriptable Option
A student in the MLBD track can, in addition, choose to enroll/satisfy the Advanced Data Science Option. To further expand students' education and create a campus-wide community, students will register for at least 4 quarters in the weekly eScience Community Seminar. Satisfying this option means that the student will have "ADS" listed on their transcript.

ML Lunch Series
A lunchtime seminar on a topic related to machine learning is held nearly weekly on Tuesdays during term. Lunch is provided. Updates are posted here.

ML Mailing List
General announcements related to machine learning are made on the ML mailing list.


(Updated September 2013)