Arizona State Master’s in Machine Learning Degree

Program Overview

Ira A. Fulton Schools of Engineering

The Master of Science in Data Science, Analytics and Engineering with a Bayesian Machine Learning concentration at Arizona State University is an intensive graduate program designed for professionals with backgrounds in computing, engineering, or quantitative fields.

The program requires 30 credit hours and can be completed in about two years of full-time study, offering either a thesis or capstone project for the culminating experience. Classes are held on-campus at the Tempe campus, and the program is not available online.

Curriculum

The curriculum provides a strong foundation in statistical and probabilistic modeling, Bayesian learning, and computational statistics. Students complete nine credit hours in core courses, nine in the concentration, and six to nine in electives, with a final three to six credit hour capstone or thesis.

Key topics include time series analysis, Bayesian decision-making, ensemble modeling, and causal modeling, preparing graduates to tackle complex data-driven challenges in industries like finance, health, and engineering.

Course details are as follows:

DSE 501 Statistics for Data Analysts
Teaches statistical inference techniques and tools, helping students apply this knowledge to data analysis in practical settings.

EEE 554 Probability and Random Processes
Applies statistical techniques to represent and analyze electrical signals and communication systems.

STP 501 Theory of Statistics I: Distribution Theory
Covers foundational probability theory, focusing on distributions, random variable transformations, order statistics, and limit concepts.

HSE 530 Intermediate Statistics for Human Systems Engineering
Explores one-way and factorial designs, contrasts, post-hoc tests, interactions, mixed designs, power, and computer applications.

CSE 511 Data Processing at Scale
Introduces large-scale data processing, including database concepts, cloud deployments, and big data tools.

CSE 512 Distributed Database Systems
Covers distributed database design, query and transaction processing, and emerging technologies.

IFT 530 Advanced Database Management Systems
Teaches advanced database concepts, security, NoSQL systems, and web databases, with practical SQL design exercises.

CSE 572 Data Mining
Explores advanced data mining topics like classification, clustering, association, and security, requiring a solid background in databases and statistics.

CSE 575 Statistical Machine Learning
Introduces advanced machine learning techniques such as clustering, regression, feature reduction, and kernel learning.

EEE 549 Statistical Machine Learning: From Theory to Practice
Focuses on algorithm design for data learning and inference, balancing theory and practical applications.

IEE 520 Statistical Learning for Data Mining
Provides a survey of data analysis methods for large data sets, with practical software-based analysis experience.

IFT 511 Analyzing Big Data
Covers data science tools and real-world applications to derive business value from big data.

MAE 551 Applied Machine Learning for Mechanical Engineers
Equips engineering students with machine learning knowledge to prepare them for evolving engineering roles.

STP 550 Statistical Machine Learning
Focuses on advanced topics like clustering, regression, feature reduction, and manifold learning.

STP 502 Theory of Statistics
Presents rigorous probability theory to strengthen the theoretical foundations of applied statistics.

STP 505 Bayesian Statistics
Explores Bayesian methods and computational tools for real-world data analysis, requiring mathematical and statistical maturity.

STP 540 Computational Statistics
Develops modern computational biostatistics skills, focusing on how and why biostatistical methods work.

STP 551 Time Series Analysis
Teaches modern univariate and multivariate time series models with Bayesian inference and Monte Carlo methods using R.

FSE 570 Data Science Capstone
Students work in interdisciplinary teams on client-driven data science projects, producing a report and presentation.

STP 599 Thesis
Supervised research for a thesis, including literature review, data collection, analysis, and writing.

More class details available here: https://catalog.apps.asu.edu/catalog/classes

Admission Requirements

To apply, candidates need a bachelor’s or master’s degree in a relevant field with a minimum 3.0 GPA in their last 60 hours or master’s program. Applicants must also show familiarity with programming tools like Python or Matlab, complete undergraduate linear algebra and statistics or probability, and provide transcripts, letters of recommendation, a resume, and a personal statement.

Tuition

For the 30-credit program, the estimated cost for an Arizona resident is $14,382, while the cost for a non-resident is $38,528.

Cost Breakdown

For Arizona Residents:

  • Base tuition: $12,939
  • Tuition surcharge: $350
  • Graduate student support fee: $290
  • Student-initiated fees: $803
  • Total estimated cost: $14,382

For Non-Residents:

  • Base tuition: $37,085
  • Tuition surcharge: $350
  • Graduate student support fee: $290
  • Student-initiated fees: $803
  • Total estimated cost: $38,528

More graudate tuition information available here: https://admission.asu.edu/cost-aid/graduate

Ideal Candidates and Career Paths

This program is best suited for data-driven professionals and engineers who want to specialize in Bayesian machine learning. It supports advanced roles in data science and analytics, including positions in finance, health systems, government, and tech industries. International students on F-1 visas may also qualify for a STEM-OPT extension for up to 24 months, broadening post-graduation career opportunities.