Stanford Root

Schedule

Stanford Root

Schedule

STATS 232

Machine Learning for Sequence Modeling (CS 229B)

UNITS:3-4
GRADING:Letter or Credit/No Credit
LEVEL:Graduate
GER:—

Sequence data and time series are becoming increasingly ubiquitous in fields as diverse as bioinformatics, neuroscience, health, environmental monitoring, finance, speech recognition/generation, video processing, and natural language processing. Machine learning has become an indispensable tool for analyzing such data; in fact, sequence models lie at the heart of recent progress in AI like GPT 3. This class integrates foundational concepts in time series analysis with modern machine learning methods for sequence modeling. Connections and key differences will be highlighted, as well as how grounding modern neural network approaches with traditional interpretations can enable powerful leaps forward. You will learn theoretical fundamentals, but the focus will be on gaining practical, hands-on experience with modern methods through real-world case studies. You will walk away with a broad and deep perspective of sequence modeling and key ways in which such data are not just 1D images.

Syllabus for selected term:
View Autumn 2026 Syllabus

Sections

1 Term
Lecture 1Open
ID: 25604
0 / 999 enrolled
DAYS:TBD
TIME:TBD
LOCATION:TBD
units

STATS 232: Machine Learning for Sequence Modeling (CS 229B)

3-4 units · Letter or Credit/No Credit

Sequence data and time series are becoming increasingly ubiquitous in fields as diverse as bioinformatics, neuroscience, health, environmental monitoring, finance, speech recognition/generation, video processing, and natural language processing. Machine learning has become an indispensable tool for analyzing such data; in fact, sequence models lie at the heart of recent progress in AI like GPT3. This class integrates foundational concepts in time series analysis with modern machine learning methods for sequence modeling. Connections and key differences will be highlighted, as well as how grounding modern neural network approaches with traditional interpretations can enable powerful leaps forward. You will learn theoretical fundamentals, but the focus will be on gaining practical, hands-on experience with modern methods through real-world case studies. You will walk away with a broad and deep perspective of sequence modeling and key ways in which such data are not just 1D images.

Offered in Autumn 2026 at Stanford University.

Autumn 2026 sections

  • Lecture — TBA TBA (Graduate)

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