Machine Learning Courses
of Colorado, Boulder
Machine learning is a subfield of Artificial Intelligence concerned
with developing computer systems that learn and make predictions
from data. Machine learning systems have shown great success
in tasks as varied as recognizing objects in images, understanding
speech, interpreting natural language texts, classifying documents by
content, guiding robots in unfamiliar environments, making product and
movie recommendations to consumers, predicting disease outbreaks,
inferring social relationships from communication patterns, and
anticipating human errors.
Machine learning provides the intelligence underlying fields sometimes
knowns as big data analytics, data science, and data mining.
Machine learning methods are widely used in engineering and
natural science fields to interpret and model data, and is increasingly
coming into play in the social sciences as well.
The University of Colorado at Boulder has a strong group of faculty in
machine learning, spread across multiple academic departments,
including computer science, applied math, electrical engineering, and
The topic of machine learning has exploded in recent years, and a
single graduate-level course cannot fully span the range of methods
used in state-of-the-art applications. We have designed a set of three
courses that can be taken in any order, depending on a student's
interests and goals. Students wishing to pursue research opportunities
and/or professional careers in machine learning
taking all three.
CSCI 5352: Network Analysis
focuses on methods for the methods for the analysis and
modeling the structure and dynamics of complex networks. This course is
distinguished from CSCI 5622 by focusing on data sets that have
relational structure readily represented by networks, an area that has
been all but ignored early in the history of machine learning.
CSCI 5622: Machine Learning
course is a survey of traditional techniques for machine
learning, including: decision trees, neural nets, support-vector
machines, boosting, sparse regression, clustering techniques, and
CSCI 5822: Probabilistic Models
course provides an in depth introduction to methods in machine learning
that are based on generative models, probabilistic inference,
and nonparametric Bayesian methods. Many but not all machine
learning algorithms can be cast
within the framework of probabilistic generative models.
CSCI 5922: Deep Learning and
course presents an in depth treatment of neural networks, covering the
history of the field from the 1960s to the present wave of enthusiasm
learning. Neural networks are
particularly effective on problems involving high-dimensional noisy
feature vectors where there is little explicit knowledge of the
processes underlying the generation of these vectors. Application
domains where deep learning and neural nets have been successfully
applied include: object recognition, image classification, speech
understanding, and natural language interpretation.
Graduate Advanced Course
CSCI 6622: Advanced Machine
course is focused on semester-long research projects of the student's
choosing, and reading and discussing research articles from the
academic literature. Prerequisite: CSCI 5352, 5622, 5822, or 5922.
CSCI 7000: Algorithmic Economics and Machine Learning
This class will explore topics in algorithmic economics and algorithmic game theory, highlighting connections and applications to theoretical machine learning. The class will alternate between lectures to give adequate background and student presentations on related research papers or additional material. Topics will include algorithmic mechanism design, social choice, online learning, information elicitation, empirical risk minimization, prediction markets, crowdsourcing mechanisms, and differential privacy.
ASEN 6519: Algorithms for Aerospace Autonomy
This advanced grad course will cover modern statistical
learning and AI techniques that allow autonomous systems to successfully
reason under uncertainty. Topics include: probabilistic models,
batch/offline learning, apporximate inference methods, sequential
optimal decision making and dynamic programming, online learning.
ECEN 5322: Analysis of
course provides an exposition of the most recent methods for
searching and analyzing high dimensional datasets. The class includes a
project: students will design and implement a content-based music
information retrieval, such as the ones used by Gracenote, Shazam, or
APPM 8500: Statistics, Optimization, and Machine Learning Seminar
This is an upper-level graduate seminar course, meeting once a week. Each
meeting will have presentations from either speakers (external or from
campus), or half-hour presentations from students. Students enrolled in
the class must give a half-hour presentation on either a research topic
(either original research or present a paper).
Related Course Offerings
Machine learning makes contact with many fields. Interested students
may wish to take courses that go into
depth in these related fields. We
list some of the courses our students have taken in the past and have
APPM 5120: Introduction to
APPM 5520: Introduction to
APPM 5540: Introduction to
APPM 5560: Markov Processes,
Queues, and Monte Carlo Simulations
APPM 5570: Statistical Methods
APPM 5720: Advanced Topics in Convex Optimization (Spring 2017)
ATLS/CSCI XXXX: Interactive machine learning for customizable and expressive interfaces (Spring 2018)
Ben Shapiro, instructor. Course number to be determined.
Course introduces students to techniques
for applying machine learning in the development of customizable
human-computer interfaces. Students will learn to process a variety
of input data (e.g., video and accelerometer stremas) using different
ML algorithms to detect semantically meaningful events that can afford
the construction of new interactive systems.
CSCI 5254: Convex Optimization
CSCI 5502: Data Mining
CSCI 5722: Computer Vision
CSCI 5832: Natural Language
CSCI 6302: Speech Recognition
CSCI 7000: Systems and Algorithms for Massive Data Applications
PSYC 5175: Computational
PSYC 7215: Reinforcement
Course Offering Schedule
What follows is a tentative plan for graduate
level ML instruction for the coming semesters.
FALL 2017: CSCI 5352, CSCI 5622, CSCI 5922
Learning at CU Boulder
list and participate in the weekly reading group. The mailing
list is used to announce readings for the week as well as job
postings. The group is open to all CU Boulder undergraduate and
Join the Data Science Team
to gain hands-on experience in machine learning and to participate in
competitions. The group is open to all CU Boulder undergraduate and graduate students.
Many departments and institutes host visiting speakers working in the
area of machine learning. Follow colloquium schedules for Computer
, Applied Math
Institute of Cognitive Science
A list of faculty interested in machine learning theory and
applications of machine learning can be found here