We’re looking for a talented UI Designer! DM @iamjiamingliu on Instagram
  • Courses
  • PSTAT
  • PSTAT 231

    • Introduction to Statistical Machine Learning . PSTAT 231

    Statistical Machine Learning is used to discover patterns and relationships in large data sets. Topics will include: data exploration, classification and regression trees, random forests, clustering and association rules. Building predictive models focusing on model selection, model comparison and performance evaluation. Emphasis will be on concepts, methods and data analysis; and students are expected to complete a significant class project, individual or team based, using real world data.

    + Add to schedule
    Prerequisites: PSTAT 120A-B; and PSTAT 126 with a minimum grade of C or better.

    4

    Units

    Optional

    Grading

    1

    Passtime

    None

    Level Limit

    Letters and science

    College
    Unlocks PSTAT 134 PSTAT 135 PSTAT 234 PSTAT 235
    These majors only stats
    Instructors
    T B A
    No info found
    Lecture
    ILP 2302
    M W
    12:30 PM - 13:45 PM
    0 / 20
    Sections
    GIRV 2123
    R
    08:00 AM - 08:50 AM
    0 / 5
    ILP 3312
    R
    09:00 AM - 09:50 AM
    0 / 5
    ILP 4107
    R
    10:00 AM - 10:50 AM
    0 / 5
    ILP 4207
    R
    11:00 AM - 11:50 AM
    0 / 5
    Enrollment Trends
    Past Enrollment Trends (5)
    See All
    Spring 2024 . Coburn K M
    NH 1006
    T R
    15:30 PM - 16:45 PM
    Fall 2024 . Baracaldo Lan
    LSB 1001
    T R
    17:00 PM - 18:15 PM
    Grading Trends (30)
    See All
    PSTAT 231 Yu G Winter 2025 Total: 5
    PSTAT 231 Coburn K M Spring 2024 Total: 29
    Other PSTAT 231 Offerings
    See All
    PSTAT 231
    0 / 20 Enrolled
    Introduction to Statistical Machine Learning
    Guo Yu 2.9
    M W
    11:00 AM - 12:15 PM
    77.4% A
    You Might Also Like
    PSTAT 207A
    0 / 40 Enrolled
    Statistical Theory
    Andrew Carter 3.1
    T R
    09:30 AM - 10:45 AM
    82.4% A
    PSTAT 210
    2 / 30 Enrolled
    Measure Theory for Probability
    Alexander Shkolnik 3.7
    T R
    12:30 PM - 13:45 PM
    80.3% A
    PSTAT 213A
    7 / 36 Enrolled
    Introduction To Probability Theory And Stochastic Processes
    Raisa Feldman 3.0
    T R
    14:00 PM - 15:15 PM
    63.8% A
    PSTAT 220A
    2 / 40 Enrolled
    Advanced Statistical Methods
    Alexander Franks 4.8
    M W
    09:30 AM - 10:45 AM
    73.0% A
    PSTAT 223A
    5 / 20 Enrolled
    STOCHASTIC CALCULUS AND APPLICATIONS
    Jean-Pierre Fouque 4.0
    T R
    11:00 AM - 12:15 PM
    88.3% A
    PSTAT 225
    12 / 30 Enrolled
    Linear and Nonlinear Mixed Effects Models
    Yuedong Wang 3.4
    M W
    17:00 PM - 18:15 PM
    93.3% A