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Statistics & Machine Learning (BHA Concentration)

Amanda Mitchell, BHA StatML Concentration Advisor (BH 129H)

BHA Statistics & Machine Learning Concentration

(87 units minimum)

In the BHA concentration in Statistics & Machine Learning, develop and master a wide array of skills in computing, mathematics, statistical theory, and the interpretation and display of complex data. In addition, students with a BHA concentration in Statistics & Machine Learning gain experience in applying statistical tools to real problems in other fields and learn the nuances of interdisciplinary collaboration. This program is geared towards students interested in statistical computation, data science or “Big Data” problems.

Prerequisites

These five courses are not counted as part of your DC Concentration. They may be used to satisfy general education or free elective requirements.

Units
21-120Differential and Integral Calculus
(prerequisite: 21-090)		10
21-127Concepts of Mathematics12
21-256Multivariate Analysis9
or 21-259 Calculus in Three Dimensions
21-240Matrix Algebra with Applications10
or 21-241 Matrices and Linear Transformations
or 21-242 Matrix Theory
15-112Fundamentals of Programming and Computer Science12

Note: 21-240, 21-241, 21-242 must be completed before taking 36-401 Modern Regression. 21-241 and 21-242 are intended only for students with a very strong mathematical background.

Statistics Core (5 courses, 45 units)
36-235Probability and Statistical Inference I -(recommended)9
or 36-225 Introduction to Probability Theory
36-236Probability and Statistical Inference II -(recommended)9
or 36-226 Introduction to Statistical Inference
36-350Statistical Computing9
36-401Modern Regression9
36-402Advanced Methods for Data Analysis9
Data Analysis Electives (1 course, 9 units)

Students must take one course from the Special Topics (numbered 36-46x–47x) and Statistics Electives listed below. Students will consult with the concentration advisor to select the Special Topics and Electives courses that best fit for their areas of interest.

36-303Sampling, Survey and Society9
36-311Statistical Analysis of Networks9
36-313Statistics of Inequality and Discrimination9
36-315Statistical Graphics and Visualization9
36-318Introduction to Causal Inference9
36-319Statistics and Machine Learning for the Physical Sciences9
36-4xx36-46x–47x Special Topics (topics and offerings vary)9
36-490Undergraduate Research9
36-493Sports Analytics Capstone9
36-497Corporate Capstone Project9
Machine Learning Core (2 courses, 24 units)
15-122Principles of Imperative Computation -(C or higher)12
10-301Introduction to Machine Learning12
or 07-280 Artificial Intelligence and Machine Learning I
Machine Learning Elective (1 course, 9 units minimum)

Students must take one course from the ML Electives listed below. Students will consult with the Statistics & Machine Learning advisor to choose an elective that best fits their area of interest. This course may have additional pre-requisites. Keep in mind this is not an exhaustive list and other applicable courses can be reviewed to be approved as an ML elective – please speak with the concentration advisor about this.

02-510/710Computational Genomics12
05-317Design of Artificial Intelligence Products12
05-434/11-344Machine Learning in Practice12
10-335Art and Machine Learning12
10-403/703Deep Reinforcement Learning & Control12
10-405/605Machine Learning with Large Datasets (Undergraduate)12
10-414Deep Learning Systems: Algorithms and Implementation12
10-417Intermediate Deep Learning12
10-418/618Machine Learning for Structured Data12
10-422Foundations of Learning, Game Theory, and Their Connections12
10-423Generative AI12
10-613Machine Learning Ethics and Society12
10-707Advanced Deep Learning12
10-708Probabilistic Graphical Models12
11-324/624Human Language for Artificial Intelligence12
11-411Natural Language Processing12
11-441Machine Learning with Graphs9
11-485Introduction to Deep Learning12
11-661Language and Statistics12
15-386Neural Computation9
15-387Computational Perception9
15-482Autonomous Agents12
16-311Introduction to Robotics12
16-385/720Computer Vision12
17-445Machine Learning in Production12
85-419Introduction to Parallel Distributed Processing9
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