B.S. in Statistics: Machine Learning Track

This track emphasizes algorithmic and theoretical aspects of statistical learning methodologies that are geared towards building predictive and explanatory models for large and complex data. It is recommended for students interested in pursuing graduate programs in statistics, machine learning, or data science, as well as for students interested in learning statistical techniques for industry. 

Notes:  

These requirements will go into effect Fall 2020.  Prior to Fall 2020, you may not graduate with a track in Machine Learning.  

Preparatory Subject Matter (27 units)

  • MAT 21A-B-C-D Calculus
  • MAT 22A Linear Algebra
  • ECS 32A or 36A Programming
    • Additional coursework in Python is also recommended (eg. ECS 32B).
  • STA 13, 32, or 100 
    • STA 32 or 100 preferred.

Depth Subject Matter (52 units)

  • STA 106 Analysis of Variance
  • STA 108 Regression Analysis
  • STA 131A Intro to Probability Theory
  • STA 131B Intro to Mathematical Statistics
  • STA 131C Intro to Mathematical Statistics
  • STA 141A Fundamentals of Statistical Data Science
  • STA 142A Statistical Learning I
  • STA 142B Statistical Learning II
  • STA 144 Sampling Theory of Surveys or STA 145 Bayesian Statistical Inference
  • MAT 167 Applied Linear Algebra or MAT 168 Optimization

Three course from:

  • STA 104 Nonparametric Statistics
  • STA 135 Multivariate Data Analysis
  • STA 137 Applied time Series Analysis
  • STA 138 Analysis of Categorical Data
  • STA 141B Data and Web Technologies for Data Analysis
  • STA 141C Big Data and High Performance Statistical Computing
  • STA 144 Sampling Theory of Surveys
  • STA 145 Bayesian Statistical Inference
  • MAT 127A Real Analysis
  • MAT 128A Numerical Analysis
  • MAT 160 Mathematics for Data Analytics and Decision Making
  • ECS 122A Algorithm Design and Analysis
  • ECS 165A Database Systems
  • ECS 158 Programming and Parallel Architectures
  • ECS 163 Information Interfaces
  • ECS 170 Introduction to Artificial Intelligence
  • ECS 174 Computer Vision
  • One approved course of 4 units from STA 199, 194HA, or 194HB may be used. 

NOTE: A course used to fulfill the core requirement cannot be used as an elective.

Total Units: 79

 

Sample Plan

Freshman

  • Fall 
    • MAT 21 A
  • Winter
    • MAT 21B
    • ECS XX
  • Spring
    • MAT 21C
    • Intro STA

Sophomore

  • Fall 
    • MAT 21D
    • MAT 022A
  • Winter
    • STA 108
  • Spring
    • STA 141A
    • STA 106

Junior

  • Fall 
    • STA 131A
    • MAT 167 or 168
  • Winter
    • STA 131B
    • STA 142A
  • Spring
    • STA 131C
    • STA 142B

Senior

  • Fall 
    • STA/ECS/MAT 1XX
  • Winter
    • STA/ECS/MAT 1XX
    • STA/ECS/MAT 1XX
  • Spring
    • STA 144 or 145