CS 780/880 Topics / Machine Learning for Sequences and Text

See course catalog entry and MyCourses for the latest information.


This course will be managed on MyCourses and Piazza.


The class covers machine learning methods to extract information from sequential data, such as human-written text and DNA sequences. Famous examples is to tag texts with parts-of-speech, extract relations expressed in text, or locate genes and gene variations in DNA sequences.

The class teaches how to derive and implement one’s own machine learning algorithm—it will not teach how to apply black-box machine learning implementations. The goal is to understand the working principles of the strongest machine learning methods for sequences we have today, such as Transformer-based Neural Networks. We will start with simple models that form the fundamentals such as Naive Bayes, Language Models, and Logistic Regression, as advanced models build on them. The class will teach how to design the ML model, derive the optimization criterion and take its derivative to implement a gradient descent optimization algorithm. Next we discuss more complex models such as conditional random fields, which require to solve to the “decoding problem”— here students will learn how to use an optimization package. Finally we cover neural networks for sequence data, especially convolutional neural networks, recurrent neural networks such as LSTMS, and transformer-based models such as BERT and XLNet. Here students will learn how to implement Neural Networks using a library such as pyTorch. As a cross-cutting topic, the class will teach fundamentals of text processing, such as tokenization and TF-IDF, as well as famous tasks in natural language processing, such as part-of-speech tagging, dependency parsing, and relation extraction.

While many ideas also apply to time series data, this will not be a major focus of this class.

Catalog Entry

This course covers basic and advanced machine learning algorithms for learning from sequential data like text or genes.

This is a synchronous lecture that requires to participate in bi-weekly homework, quizzes, and code presentations throughout the semester.

The course is independent of other Machine Learning and Data Science classes. However these be used to complement and deepen the student’s machine learning skills.

Courses marked with CS7xx/8xx have not yet received their permanent course number, but are currently listed as CS780/880 different sections denote different courses.


Data Structures (CS 515) or permission of instructor. Ability to independently write programs in either Python.

For students interested in taking the follow-up course CS953 “Data science for Knowledge Graphs and Text”, either this course or CS853 will satisfy the prereqs.

Grading Policy

  1. Final class project (50% of final grade): Students choose an application to develop a machine learning algorithm with data loading and evaluation on a topic of their choice.

  2. Weekly quizzes (40% of final grade): Short 10 minute quizzes at the beginning of class.

  3. Final exam (10% of final grade): Written exam to test knowledge gained throughout the semester.

  4. Bi-weekly homework assignments (written and programming) are mandatory but graded pass-fail-excellent scale. A pass or excellent needs to be obtained on at least four (out of five) homework assignments in order to be eligible for participation in the final exam and submission of a final project report. Students with two or more excellents obtain an upgrade to the next higher letter grade (e.g., B- to B, or B+ to A-).

  5. Code reviews on programming homework/project: If the code review is missed or student is unable to explain their code, the homework/project grade will be reduced to “F”.

The same grading policy applies to both students taking the course as CS 7xx and CS 8xx. Of course, expectations for students taking the course for graduate credits under CS 8xx are higher.

Late homework and project report submissions will generally be excluded. Any missed activity due to medical or families emergencies requires supporting documentation through the dean’s office or SAS etc.

Academic Integrity

The instructor is strongly committed to upholding the standards of academic integrity. These standards, at the minimum, require that students never present the work of others as their own. Any dishonest behavior, once discovered, will be penalized according to the University’s Student Code of Conduct.

Mutual Expectations

Students are expected to:

The instructor is expected to:

Note that is not sufficient to just be present in class and submit homeworks. Obtaining an A requires that you study and review materials from lecture notes, assignments, and discussions with the help of the book. If stuck, please see the instructor.


No textbook is available. An earlier edition of the class can be found online: https://gitlab.cs.unh.edu/mlseq/lecture-materials/tree/master/slides


Note that this schedule is preliminary and will possibly change as the course progresses. Chapter references are based on the book Introduction to Information Retrieval (IIS).

An earlier edition of a similar course was taught at Mannheim University.

Important Dates

Quizzes: TBP, see “MyCourses” Final exam: TBD, see “MyCourses”

ABET Learning Outcome Classification

1. Analyze a complex computing problem and to apply principles of computing and other relevant disciplines to identify solutions.

2. Design, implement, and evaluate a computing-based solution to meet a given set of computing requirements in the context of the program’s discipline.

6. Apply computer science theory and software development fundamentals to produce computing-based solutions.

ABET Curriculum Classification

4. Substantial coverage of a) algorithms and complexity, b) computer science theory, c) concepts of programming languages, and d) software development.

Covered by:

  1. Algorithms for Machine Learning and NLP
  1. Theory
  1. Programming
  1. Class project on Domain-specific Application