CU Boulder MS-CS on Coursera Enrollment

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Learn at your own pace and power your future with this fully online Master of Science in Computer Science (MS-CS).

Students are led by the same award-winning faculty teaching on campus and receive the same diploma as our on-campus Professional MS-CS.

Learn more about the MS-CS on Coursera Opens in new window

university of colorado boulder student computer science department

Are you a current or returning student with a CU login name and password (IdentiKey)?

cu boulder on coursera computer science recommended learner journey

You may take courses in any order. We recommend the following learner journey:

Complete one full pathway (3 credits) with a B or better in each class to earn admission to the degree. Choose either:
- Foundations of Data Structures and Algorithms pathway OR
- Network Systems: Principles and Practice (Linux and Cloud Networking) (Pathway)
Complete remaining breadth courses (12 credits) and elective courses (15 credits) in any order. For electives, choose from either:
- 4 three-credit specializations + 3 one-credit courses
- 5 three-credit specializations
Graduate with your master’s degree.

Looking for something different? Design your own learner journey! Start with whatever interests you most — perhaps a breadth course on machine learning or an elective on data mining. Then, complete all three courses within one of our pathway specializations with a cumulative 3.0 GPA or better when you're ready to earn admission to the program. Credits you earn before admission will apply toward the degree. Note that you must complete all courses within 8 years.

All courses below count toward the MS-CS degree. See MS-CS on Coursera Curriculum opens in new window for course details.

Upgrading from the Non-credit Experience to Earn Credit
If you have completed and/or made progress in any courses in the non-credit experience, you may use this enrollment form to upgrade to the for-credit experience. After completing the enrollment process and the onboarding steps, your progress from the non-credit version will transfer with you (except interactive items like discussion board posts and peer-graded assignments). Then, you will need to complete additional for-credit material before the posted deadlines.

What to Expect After Submitting This Form
You will need to complete all steps below to access for-credit content.

Select courses on this enrollment form and pay tuition
First session only: Activate your IdentiKey (Instructions will be sent via email by the Office of Information Technology within 48 hours of enrollment)
First session only: Activate your CU Boulder email account
First session only: Link your CU email account to your Coursera account
First session only: Complete onboarding course
Access for-credit content in the ‘My Courses’ section of your Coursera account once the session begins and complete all coursework by the posted deadlines)
Upgrades only: Complete additional for-credit content by the posted deadlines. Though your progress from the non-credit version of the course will transfer to your for-credit experience, you will need to complete additional material in the for-credit version.

Course Selection

Click on the course name below to select from our available courses.

You may select up to three courses per enrollment form. There is no limit to the number of enrollment forms you may submit per session.
We recommend that new students take one course at a time to start.
You may return and enroll in more courses at any point before the current enrollment window closes on June 13, 2025.
See MS-CS Calendar opens in new window for deadlines to complete peer-graded assignments, review your peers, schedule and complete ProctorU exams, complete other exams, and submit all coursework.
Note that some courses are offered under two or more programs (e.g., Dynamic Programming, Greedy Algorithms is offered as both CSCA 5414 and DTSA 5503). You may not earn credit for more than one version of a cross-listed course.

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tuition - takes the list of tuitions and sums them together for total tuition real number. --MSDS

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Number of courses:

Tuition prices may vary depending on program.

Please see AI Certificate requirements here.

You have selected the maximum (3) courses allowed per enrollment form. If you would like to change a selected course, uncheck the course name. Please complete another enrollment form to sign up for additional courses. We recommend contacting a graduate advisor at [email protected] if you have questions about your schedule.

Computer Science Courses

Pathway Courses

Foundations of Data Structures and Algorithms (Pathway)

CSCA 5414 Dynamic Programming Greedy Algorithms (1 credit)

CSCA 5424 Approximation Algorithms and Linear Programming (1 credit)

CSCA 5454 Advanced Data Structures RSA and Quantum Algorithms (1 credit)

Network Systems: Principles and Practice (Linux and Cloud Networking) (Pathway)

CSCA 5063 Network Systems Foundation (1 credit)

CSCA 5073 Linux Networking (1 credit)

CSCA 5083 Cloud Networking (1 credit)

Breadth Courses

Machine Learning: Theory and Hands-on Practice with Python (Breadth)

CSCA 5622 Introduction to Machine Learning - Supervised Learning (1 credit)

CSCA 5632 Unsupervised Algorithms in Machine Learning (1 credit)

CSCA 5642 Introduction to Deep Learning (1 credit)

Computing, Ethics, and Society (Breadth)

CSCA 5214 Computing Ethics and Society Foundations (1 credit)

CSCA 5224 Ethical Issues in AI and Professional Ethics (1 credit)

CSCA 5234 Ethical Issues in Computing Applications (1 credit)

Foundations of Autonomous Systems (Breadth)

CSCA 5834 Modeling of Autonomous Systems (1 credit)

CSCA 5844 Requirement Specifications for Autonomous Systems (1 credit)

CSCA 5854 Verification and Synthesis of Autonomous Systems (1 credit)

Elective Courses

Software Architecture for Big Data (Elective)

CSCA 5008 Fundamentals of Software Architecture for Big Data (1 credit)

CSCA 5018 Software Architecture Patterns for Big Data (1 credit)

CSCA 5028 Applications of Software Architecture for Big Data (1 credit)

Data Mining Foundations and Practice (Elective)

CSCA 5502 Data Mining Pipeline (1 credit)

CSCA 5512 Data Mining Methods (1 credit)

CSCA 5522 Data Mining Project (1 credit)

Natural Language Processing: Deep Learning Meets Linguistics (Elective)

CSCA 5832 Fundamentals of Natural Language Processing (1 credit)

CSCA 5842 Deep Learning for Natural Language Processing (1 credit)

Generative AI (Elective)

CSCA 5112 Introduction to Generative AI (1 credit)

Introduction to Computer Vision (Elective)

CSCA 5222 Introduction to Computer Vision (1 credit)

Internet Policy: Principles and Problems (Elective)

CSCA 5433 When to Regulate? The Digital Divide and Net Neutrality (1 credit)

Introduction to Robotics with Webots (Elective)

CSCA 5312 Basic Robotic Behaviors and Odometry (1 credit)

CSCA 5332 Robotic Mapping and Trajectory Generation (1 credit)

CSCA 5342 Robotic Path Planning and Task Execution (1 credit)

Fundamentals of Data Visualization (standalone course) (Elective)

CSCA 5702 Fundamentals of Data Visualization (1 credit)

Object-Oriented Analysis & Design (Elective)

CSCA 5428 Object-Oriented Analysis and Design: Foundations and Concepts (1 credit)

CSCA 5438 Object-Oriented Analysis and Design: Patterns and Principles (1 credit)

CSCA 5448 Object-Oriented Analysis and Design: Practice and Architecture (1 credit)

Security and Ethical Hacking (Elective)

CSCA 5303 Attacking the Network (1 credit)

CSCA 5313 Attacking Unix & Windows (1 credit)

Outside Electives (optional)

Below, you will find a list of courses offered by other CU programs on Coursera that may be applied toward the MS-CS degree. You may apply up to 6 credits from this list toward your MS-CS elective requirements. Please note that course tuition may vary depending on the program.

Data Science Courses

Data Science Foundations: Statistical Inference (Outside Elective)

DTSA 5001 Probability Theory - Foundation for Data Science (1 credit)

DTSA 5002 Statistical Inference for Estimation in Data Science (1 credit)

DTSA 5003 Statistical Inference and Hypothesis Testing in Data Science Applications (1 credit)

Vital Skills for Data Science (Outside Elective)

DTSA 5301 Data Science as a Field (1 credit)

Statistical Modeling for Data Science Applications (Outside Elective)

DTSA 5011 Modern Regression Analysis in R (1 credit)

DTSA 5012 ANOVA and Experimental Design (1 credit)

DTSA 5013 Generalized Linear Models and Nonparametric Regression (1 credit)

Databases for Data Scientists (Outside Elective)

DTSA 5733 Relational Database Design (1 credit)

DTSA 5734 The Structured Query Language (SQL) (1 credit)

DTSA 5735 Advanced Topics and Future Trends in Database Technologies - Elective (1 credit)

Data Science Methods for Quality Improvement (Outside Elective)

DTSA 5704 Managing Describing and Analyzing Data (1 credit)

DTSA 5705 Stability and Capability in Quality Improvement (1 credit)

DTSA 5706 Measurement System Analysis (1 credit)

Effective Communication (Outside Elective)

DTSA 5842 Effective Communication: Writing Design and Presentation (1 credit)

DTSA 5843 Effective Communication Capstone Project (1 credit)

Statistical Learning for Data Science (Outside Elective)

DTSA 5020 Regression and Classification (1 credit)

DTSA 5021 Resampling Selection and Splines (1 credit)

DTSA 5022 Trees SVM and Unsupervised Learning (1 credit)

Text Marketing Analytics (Outside Elective)

DTSA 5798 Supervised Text Classification for Marketing Analytics (1 credit)

DTSA 5799 Unsupervised Text Classification for Marketing Analytics (1 credit)

DTSA 5800 Network Analysis for Marketing Analytics (1 credit)

IBM Capstone Project (Outside Elective)

DTSA 5841 IBM Capstone Project (1 credit)

Modeling and Predicting Climate Anomalies (Outside Elective)

DTSA 5740 Global Climate Change Policies and Analysis (1 credit)

DTSA 5741 Modeling Climate Anomalies with Multivariate Regression (1 credit)

DTSA 5742 Predicting Extreme Climate Behavior with Machine Learning (1 credit)

Bayesian Statistics for Data Science (Outside Elective)

DTSA 5726 Introduction to Bayesian Statistics for Data Science Applications (1 credit)

Engineering Management Courses

Finance for Technical Managers (Outside Elective)

EMEA 5021 Product Cost & Investment Cash Flow Analysis (1 credit)

EMEA 5022 Project Valuation and the Capital Budgeting Process (1 credit)

EMEA 5023 Financial Forecasting and Reporting (1 credit)

Project Management (Outside Elective)

EMEA 5031 Project Management: Foundations and Initiation (1 credit)

EMEA 5032 Project Planning and Execution (1 credit)

EMEA 5033 Agile Project Management (1 credit)

Principles of Leadership: Leading Oneself (Outside Elective)

EMEA 5051 Leading Oneself with Self-Knowledge (1 credit)

EMEA 5052 Leading Oneself with Purpose and Meaning (1 credit)

EMEA 5053 Leading Oneself with Personal Excellence (1 credit)

Technical Communication (Outside Elective)

EMEA 5016 Communication as a Technical Leader (1 credit)

EMEA 5017 Technical Managerial Written Skills (1 credit)

EMEA 5018 Speaking to a Technical Group (1 credit)

Principles of Leadership: Leading Technical Organizations (Outside Elective)

EMEA 5054 Leadership Style and Building a High Performance Team (1 credit)

EMEA 5055 Accountability and Employee Engagement (1 credit)

EMEA 5056 Value Creation and Building Enduring Relationships (1 credit)

Global Perspectives of Diversity, Equity, and Inclusion in the Workplace (Outside Elective)

EMEA 5057 Your World and What Shapes It (1 credit)

EMEA 5058 Their World and How You Define It (1 credit)

EMEA 5059 Our World and How to Accept It (1 credit)

Technology Entrepreneurship (Outside Elective)

EMEA 5091 Getting Started with Technology Startups (1 credit)

EMEA 5092 Creating a Technology Startup Company (1 credit)

EMEA 5093 Forming Funding and Launching a Technology Startup Company (1 credit)

The Data Driven Manager (Outside Elective)

EMEA 5006 Defining Describing and Visualizing Data (1 credit)

EMEA 5007 Data Acquisition Risk and Estimation (1 credit)

EMEA 5008 Data Driven Decision Making (1 credit)

Principles of Leadership: Leading Technical Teams (Outside Elective)

EMEA 5061 A Technical Leaders Qualities and Effectiveness (1 credit)

EMEA 5062 Challenges of Leading Individuals in the Tech Industry (1 credit)

EMEA 5063 Challenges of Leading Technical Teams (1 credit)

Introduction to Systems Engineering (Outside Elective)

EMEA 5034 The Need for Systems Engineering (1 credit)

EMEA 5035 Applying Systems Engineering to the Design Process (1 credit)

EMEA 5036 Systems Engineering and Program Management (1 credit)

Neuroscience of Personal Excellence (Outside Elective)

EMEA 5064 The Neuroscience of Personal Excellence (1 credit)

EMEA 5065 The Neuroscience of Leading High-Performance Teams (1 credit)

EMEA 5066 The Neuroscience of Leading Transformational Organizations (1 credit)

Marketing Strategy for Engineers and Technologists (Outside Elective)

EMEA 5094 Market Research & Analysis for Tech Industries (1 credit)

EMEA 5095 Digital Media & Strategic Planning in Technology Markets (1 credit)

EMEA 5096 Building and Pitching Marketing Campaigns in Tech Industries (1 credit)

Ethical Decision-Making in the Tech Industry (Outside Elective)

EMEA 5081 A Theoretical Origin of Ethics in Business and Tech Industry (1 credit)

EMEA 5082 Avoiding Ethical Pitfalls in the Tech Industry (1 credit)

EMEA 5083 Ethical Decision Making for Success in the Tech Industry (1 credit)

Product Development (Outside Elective)

EMEA 5401 Strategic Product Development (1 credit)

EMEA 5402 Managing the New Product Development Process (1 credit)

EMEA 5403 Product Innovation Management (1 credit)

Resilience Engineering and Leadership in Crisis (Outside Elective)

EMEA 5231 Resilience and Leadership: Concepts Definitions and Frameworks (1 credit)

EMEA 5232 Resilience and Leadership: Tools Methods and Applications (1 credit)

EMEA 5233 Resilience and Leadership: Design Development and Integration (1 credit)

Applied Sustainability for Technical Managers (Outside Elective)

EMEA 5216 Sustainability and the Circular Economy (1 credit)

EMEA 5217 Applied Sustainability Engineering (1 credit)

EMEA 5218 Leading the Circular and Sustainable Business (1 credit)

Sustainable and Resilient Operations and Supply Chains (Outside Elective)

EMEA 5226 Sustainable and Resilient Operations Management (1 credit)

EMEA 5227 Developing and Managing Sustainable Supply Chains (1 credit)

EMEA 5228 Impacts of Sustainable Operations and Supply Chains (1 credit)

Sustainable and Circular Product Development (Outside Elective)

EMEA 5222 Product Design for the Circular Economy (1 credit)

EMEA 5223 Packaging Design for the Circular Economy (1 credit)

EMEA 5224 Circular Product Design Frameworks and Certifications (1 credit)

Transformative Leadership in the Circular Economy (Outside Elective)

EMEA 5241 The Circular Economy (1 credit)

Electrical Engineering Courses

Active Optical Devices (Outside Elective)

ECEA 5605 Light Emitting Diodes and Semiconductor Lasers (1.2 credits)

ECEA 5606 Nanophotonics and Detectors (1.2 credits)

ECEA 5607 Displays (0.6 credits)

Algorithms for Battery Management Systems (Outside Elective)

ECEA 5730 Introduction to Battery-Management Systems (0.8 credits)

ECEA 5731 Equivalent Circuit Cell Model Simulation (0.8 credits)

ECEA 5732 Battery State-of-Charge (SOC) Estimation (1 credit)

ECEA 5733 Battery State-of-Health (SOH) Estimation (0.8 credits)

ECEA 5734 Battery Pack Balancing and Power Estimation (0.8 credits)

Developing Industrial Internet of Things (Outside Elective)

ECEA 5385 Industrial IoT Markets and Security (1 credit)

ECEA 5386 Project Planning and Machine Learning (1 credit)

ECEA 5387 Modeling and Debugging Embedded Systems (1 credit)

Embedded Interface Design (Outside Elective)

ECEA 5346 User Experience Interface Design for Embedded Systems (1 credit)

ECEA 5347 Rapid Prototyping of Embedded Interface Designs (1 credit)

ECEA 5348 M2M IoT I/F Design & Protocols (1 credit)

Embedding Sensors and Motors (Outside Elective)

ECEA 5340 Sensors and Sensor Circuit Design (0.8 credits)

ECEA 5341 Motors and Motor Control Circuits (0.8 credits)

ECEA 5342 Pressure Force Motion and Humidity Sensors (0.8 credits)

ECEA 5343 Sensor Manufacturing and Process Control (0.6 credits)

FPGA Design for Embedded Systems (Outside Elective)

ECEA 5360 Introduction to FPGA Design for Embedded Systems (0.8 credits)

ECEA 5361 Hardware Description Languages for FPGA Design (0.8 credits)

ECEA 5362 FPGA Softcore Processors and IP Acquisition (0.8 credits)

ECEA 5363 Building FPGA Projects (0.6 credits)

Modeling and Control of Power Electronics (Outside Elective)

ECEA 5705 Averaged Switch Modeling and Simulation (0.8 credits)

ECEA 5706 Technical Design-Oriented Analysis (0.6 credits)

ECEA 5707 Input Filter Design (0.6 credits)

ECEA 5708 Current Mode Control (1.2 credits)

ECEA 5709 Modeling and Control of Single-Phase Rectifiers and Inverters (0.6 credits)

Optical Engineering (Outside Elective)

ECEA 5600 First Order Optical System Design (1 credit)

ECEA 5601 Optical Efficiency and Resolution (1 credit)

ECEA 5602 Design of High-Performance Optical Systems (1 credit)

Power Electronics (Outside Elective)

ECEA 5700 Introduction to Power Electronics (0.8 credits)

ECEA 5701 Converter Circuits (1 credit)

ECEA 5702 Converter Control (1.2 credits)

ECEA 5703 Magnetics Design (1 credit)

Power Electronics Capstone (Outside Elective)

ECEA 5715 Capstone Design Project in Power Electronics (1.2 credits)

Real-time Embedded Systems (Outside Elective)

ECEA 5315 Concepts and Practices (0.6 credits)

ECEA 5316 Theory and Analysis (0.8 credits)

ECEA 5317 Mission-Critical - SW Application (0.8 credits)

ECEA 5318 Real-Time Emb System Project (0.8 credits)

Semiconductor Devices (Outside Elective)

ECEA 5630 Semiconductor Physics (1 credit)

ECEA 5631 Diode: pn Junction and Metal Semiconductor Contact (1 credit)

ECEA 5632 Transistor: Field Effect Transistor and Bipolar Junction Transistor (1 credit)

Photovoltaic Power Electronics (Outside Elective)

ECEA 5716 Open-Loop Photovoltaic Power Electronics Laboratory (1 credit)

ECEA 5717 Closed-Loop Photovoltaic Power Electronics Laboratory (1 credit)

ECEA 5718 Photovoltaic Power Electronics Battery Management Laboratory (1 credit)

Quantum Mechanics for Engineers (Outside Elective)

ECEA 5610 Foundations of Quantum Mechanics (1.4 credit)

ECEA 5611 Theory of Angular Momentum (0.8 credit)

ECEA 5612 Approximation Methods (0.8 credit)

Advanced Embedded Linux Development (Outside Elective)

ECEA 5305 Linux System Programming and Introduction to Buildroot (1 credit)

ECEA 5306 Linux Kernel Programming and Introduction to Yocto (1 credit)

ECEA 5307 Embedded Systems Topics and Project (1 credit)

Power Semiconductor Devices (Outside Elective)

ECEA 5721 Introduction to Power Switches (0.6 credits)

ECEA 5722 High-Voltage p-n and Schottky Diodes (1.2 credits)

Sensors for a Carbon Free World (Outside Elective)

ECEA 5349 Electric Vehicle Sensors (1 credit)

ECEA 5350 Wind Turbine Sensors (1 credit)

ECEA 5351 Solar Power Sensors (1 credit)

Control Systems Analysis (Outside Elective)

ECEA 5800 Control Systems Analysis: Modeling of Feedback Systems (1 credit)

Engineering Genetic Circuits (Outside Elective)

ECEA 5934 Engineering Genetic Circuits: Design (1 credit)

ECEA 5935 Engineering Genetic Circuits: Modeling and Analysis (1 credit)

ECEA 5936 Engineering Genetic Circuits: Abstraction Methods (1 credit)

Fundamentals of Model Checking (Outside Elective)

ECEA 5900 Introduction to Modeling for Formal Verification (1 credit)

ECEA 5901 Temporal Logic Model Checking (1 credit)

Applied Kalman Filtering (Outside Elective)

ECEA 5850 Kalman-Filter Boot Camp and State-Estimation Application (0.8 credit)

ECEA 5851 Kalman Filter Deep Dive and Target-Tracking Application (0.8 credit)

ECEA 5852 Nonlinear Kalman Filters Parameter-Estimation Application (0.8 credit)

ECEA 5853 Particle Filters and Navigation Application (0.8 credit)

CSCA 5414 Dynamic Programming, Greedy Algorithms
Specialization: Foundations of Data Structures and Algorithms
Instructor: Sriram Sankaranarayanan, Ph.D., Co-Associate Chair for Undergraduate Education, Professor of Computer Science

See Coursera opens in new window and syllabus opens in new window for detailed description, including any required materials.

This course covers basic algorithm design techniques such as divide and conquer, dynamic programming, and greedy algorithms. It concludes with a brief introduction to intractability (NP-completeness) and using linear/integer programming solvers for solving optimization problems. Cross listed with DTSA 5503.

CSCA 5424 Approximation Algorithms and Linear Programming
Specialization: Foundations of Data Structures and Algorithms
Instructor: Sriram Sankaranarayanan, Ph.D., Co-Associate Chair for Undergraduate Education, Professor of Computer Science

See Coursera and syllabus opens in new window for detailed description, including any required materials.

Covers ideas surrounding approximation algorithms including a rigorous mathematical analysis of the approximation guarantees provided by these algorithms. Teaches the use of linear/integer programming formulations for common algorithmic problems and the relation between integer optimization problems and their linear programming relaxations. Introduces key mathematical concepts needed to analyze these algorithms and explores the application of algorithmic concepts to real-world problems.

CSCA 5454 Advanced Data Structures, RSA and Quantum Algorithms
Specialization: Foundations of Data Structures and Algorithms
Instructor: Sriram Sankaranarayanan, Ph.D., Co-Associate Chair for Undergraduate Education, Professor of Computer Science

See Coursera opens in new window and syllabus opens in new window for detailed description, including any required materials.

Covers advanced ideas in data structures such as B-Trees and Fibonacci heaps while presenting further applications of amortized analyses. Introduces number theoretic algorithms that form the basis of RSA public-key cryptography. Provides a brief introduction to quantum computing/algorithms by teaching the basics of quantum computation and two important examples of efficient quantum algorithms. Introduces key mathematical concepts needed to analyze these algorithms and explores the application of algorithmic concepts to real-world problems.

CSCA 5008 Fundamentals of Software Architecture for Big Data
Specialization: Software Architecture for Big Data
Instructor: Mike Barinek, Lecturer & Tyson Gern, Delivery Lead at VMware Tanzu Labs

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The course is intended for individuals looking to understand the basics of software engineering as they relate to building large software systems that leverage big data. Learners will be introduced to software engineering concepts necessary to build and scale large, data intensive, distributed systems. Starting with software engineering best practices and loosely coupled, highly cohesive data microservices, the course takes learners through the evolution of a distributed system over time. Cross listed with DTSA 5507.

CSCA 5018 Software Architecture Patterns for Big Data
Specialization: Software Architecture for Big Data
Instructor: Mike Barinek, Lecturer & Tyson Gern, Delivery Lead at VMware Tanzu Labs

See Coursera and syllabus opens in new window for detailed description, including any required materials.

The course is intended for individuals looking to understand the architecture patterns necessary to take large software systems that leverage big data to production. Learners will transform big data prototypes into high quality tested production software. After measuring the performance characteristics of distributed systems, learners will identify trouble areas and implement scalable solutions to improve performance. Upon completion of the course learners will know how to scale production datastores to perform under load, designing load tests to ensure applications meet performance requirements. Cross listed with DTSA 5508.

CSCA 5028 Applications of Software Architecture for Big Data
Specialization: Software Architecture for Big Data
Instructor: Mike Barinek, Lecturer & Tyson Gern, Delivery Lead at VMware Tanzu Labs

See Coursera and syllabus opens in new window for detailed description, including any required materials.

The course is intended for individuals who want to build a production-quality software system that leverages big data. Learners will apply the basics of software engineering and architecture to create a production-ready distributed system that handles big data. Learners will build and scale a large, data intensive, distributed system, composed of loosely coupled, highly cohesive data microservices. Cross listed with DTSA 55714.

CSCA 5622 Introduction to Machine Learning - Supervised Learning
Specialization: Machine Learning: Theory and Hands-on Practice with Python
Instructor: Geena Kim, Instructor

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This course introduces various supervised ML algorithms and prediction tasks applied to different data. Specific topics include linear and logistic regression, KNN, Decision trees, ensemble methods such as Random Forest and Boosting, and kernel methods such as SVM. Cross listed with DTSA 5509.

CSCA 5632 Unsupervised Algorithms in Machine Learning
Specialization: Machine Learning: Theory and Hands-on Practice with Python
Instructor: Geena Kim, Instructor

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In this course, we will learn selected unsupervised learning methods for dimensionality reduction, clustering, finding latent features, and application cases such as recommender systems with hands-on examples of product recommendation algorithms. Cross listed with DTSA 5510.

CSCA 5642 Introduction to Deep Learning
Specialization: Machine Learning: Theory and Hands-on Practice with Python
Instructor: Geena Kim, Instructor

See Coursera and syllabus opens in new window for detailed description, including any required materials.

Course will cover the basics of deep learning, such as multilayer perceptron, convolutional neural network, recurrent neural network, how to build and train neural network models, optimization methods, and application examples Cross listed with DTSA 5511.

CSCA 5214 Computing, Ethics, and Society Foundations
Specialization: Computing, Ethics, and Society
Instructor: Bobby Schnabel,Professor, Department External Chair

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Computing systems and technologies fundamentally impact the lives of most people in the world, including how we communicate, get information, socialize, and receive healthcare. This course is the first of a three course sequence that examines ethical issues in the design and implementation of computing systems and technologies, and reflects upon the broad implication of computing on our society. It covers ethical theories, privacy, security, social media, and misinformation.

CSCA 5224 Ethical Issues in AI and Professional Ethics
Specialization: Computing, Ethics, and Society
Instructor: Bobby Schnabel,Professor, Department External Chair

See Coursera and syllabus opens in new window for detailed description, including any required materials.

Computing systems and technologies fundamentally impact the lives of most people in the world, including how we communicate, get information, socialize, and receive healthcare. This course is the second of a three course sequence that examines ethical issues in the design and implementation of computing systems and technologies, and reflects upon the broad implication of computing on our society. It covers algorithmic bias in machine learning methods, professional ethics, and issues in the tech workplace.

EMEA 5241 The Circular Economy
Specialization: Transformative Leadership in the Circular Economy
Instructor: Christy Bozic, Professor of Engineering Management

See Syllabus for detailed description, including any required materials.

This course defines the Circular Economy and how it differs from the linear economy. It then highlights the need to move toward a Circular Economy, and how such a transition could take place. This also requires customers going from being “consumers” to “users”, which impacts product design, and therefore challenges existing financial business models. The course concludes with several case studies highlighting companies successfully adopting Circular design practices.

CSCA 5234 Ethical Issues in Computing Applications
Specialization: Computing, Ethics, and Society
Instructor: Bobby Schnabel,Professor, Department External Chair

See Coursera and syllabus opens in new window for detailed description, including any required materials.

Computing systems and technologies fundamentally impact the lives of most people in the world, including how we communicate, get information, socialize, and receive healthcare. This course is the third of a three course sequence that examines ethical issues in the design and implementation of computing systems and technologies, and reflects upon the broad implication of computing on our society. It covers medical applications, uses of robotics, autonomous vehicles, and the future of work.

CSCA 5502 Data Mining Pipeline
Specialization: Data Mining Foundations and Practice
Instructor: Qin (Christine) Lv, Ph.D., Professor of Computer Science

See Coursera and syllabus opens in new window for detailed description, including any required materials.

This course introduces the key steps involved in the data mining pipeline, including data understanding, data preprocessing, data warehouse, data modeling, interpretation and evaluation, and real-world applications. Cross listed with DTSA 5504.

CSCA 5512 Data Mining Methods
Specialization: Data Mining Foundations and Practice
Instructor: Qin (Christine) Lv, Ph.D., Professor of Computer Science

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This course covers core techniques used in data mining, including frequent pattern analysis, classification, clustering, outlier detection, as well as time-series mining and graph mining. Cross listed with DTSA 5505.

CSCA 5522 Data Mining Project
Specialization: Data Mining Foundations and Practice
Instructor: Qin (Christine) Lv, Ph.D., Professor of Computer Science

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This course offers step-by-step guidance and hands-on experience of designing and implementing a real-world data mining project, including problem formulation, literature survey, proposed work, evaluation, discussion and future work. Cross listed with DTSA 5506.

CSCA 5832 Fundamentals of Natural Language Processing
Specialization: Natural Language Processing: Deep Learning Meets Linguistics
Instructor: Jim Martin, Ph.D., Professor of Computer Science

See Coursera opens in new window and syllabus opens in new window for detailed description, including any required materials.

The field of natural language processing aims at getting computers to perform useful and interesting tasks with human language. This course introduces students to the fundamental problems in NLP, the fundamental techniques that are used to solve those problems and lays the foundation for understanding state-of-art methods. At the end of the course, students will be able to implement and analyze text classifiers, sequence labelers, discrete probabilistic models, and vector-based approaches to word meaning.

CSCA 5842 Deep Learning for Natural Language Processing
Specialization: Natural Language Processing: Deep Learning Meets Linguistics
Instructor: Jim Martin, Ph.D., Professor of Computer Science

See Coursera opens in new window and syllabus opens in new window for detailed description, including any required materials.

Deep learning has revolutionized the field of natural language processing and led to many state-of-the-art results. This course introduces students to neural network models and training algorithms frequently used in natural language processing. At the end of this course, learners will be able to explain and implement feedforward networks, recurrent neural networks, convolutional neural networks, and transformers. They will also have an understanding of transfer learning, the paradigm behind popular models such as BERT and GPT-3.

CSCA 5112 Introduction to Generative AI
Specialization: Generative AI
Instructor: Tom Yeh, Ph.D., Associate Professor of Computer Science

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In this course, students will learn about several topics related to Generative AI, including deep learning and machine learning algorithms that enable machines to generate text, images, and music. Additionally, they will also learn about the diffusion model and transformer model, which are important techniques used in Generative AI. The course will guide students on how to apply these techniques to design and build their own generative models and apply those models to new problems.

CSCA 5222 Introduction to Computer Vision
Specialization: Introduction to Computer Vision
Instructor: Tom Yeh, Ph.D., Associate Professor of Computer Science

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This course guides students through the essential algorithms and methods to help computers 'see' and interpret visual data. Students learn the core concepts and techniques that have been traditionally used to analyze images. Then, students learn modern deep learning methods, such as neural networks and specific models designed for image recognition, can be used to perform more complex tasks like object detection and image segmentation. Additionally, students will learn the creation and impact of AI-generated images and videos, exploring the ethical considerations of such technology.

CSCA 5433 When to Regulate? The Digital Divide and Net Neutrality
Specialization: Internet Policy: Principles and Problems
Instructor: David Reed, Ph.D., Scholar in Residence

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This is the first out of three courses exploring Internet Policy: Principles and Problems, which is part of CU Boulder’s Masters of Science in Data Science program on Coursera. This course builds an interdisciplinary policy framework to critique and develop regulatory approaches to real-world problems on the Internet. Learners then use the framework to develop a definition of broadband to improve the Digital Divide and to evaluate net neutrality regulations. Cross listed with DTSA 5736.

CSCA 5312 Basic Robotic Behaviors and Odometry
Specialization: Introduction to Robotics with Webots
Instructor: Nikolaus Correll, Ph.D., Professor of Computer Science

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Introduction to autonomous mobile robots, including forward kinematics (“odometry”), basic sensors and actuator, and simple reactive behavior. The course is centered around two laboratory exercises in the realistic, physics-based simulator “Webots” in which students will experiment with simple reactive behaviors for collision avoidance and line following, state machines, and basic forward kinematics of non-holonomic systems. An overarching objective of this course is to understand the role of the physical system on algorithm design and its role as source of uncertainty that makes robots non-deterministic.

CSCA 5332 Robotic Mapping and Trajectory Generation
Specialization: Introduction to Robotics with Webots
Instructor: Nikolaus Correll, Ph.D., Professor of Computer Science

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Building upon the course “Basic Robotic Behaviors and Odometry”, students will learn how to perform basic inverse kinematics of (non-)holonomic systems using a feedback control approach and how to process multi-dimensional sensor signals such as laser range scanners to create discrete representations of the environment (mapping). Also in this course, the overarching focus is mechanisms and sensors as sources of uncertainty and techniques to model and control for them.

CSCA 5342 Robotic Path Planning and Task Execution
Specialization: Introduction to Robotics with Webots
Instructor: Nikolaus Correll, Ph.D., Professor of Computer Science

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Building upon the courses “Basic Robotic Behaviors and Odometry” and “Robotic Mapping and Trajectory Generation”, students will learn how implement high-level reasoning for generating trajectories (path planning) and sequencing tasks under uncertainty of sensing and actuation. As a first cap stone in the robotics specialization, this course will also lead toward the implementation of a complex mobile manipulation system, combining behaviors, sensing, control and planning developed in previous modules.

CSCA 5702 Fundamentals of Data Visualization
Specialization: Fundamentals of Data Visualization
Instructor: Danielle Szafir, Ph.D., Assistant Professor of Computer Science

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Fundamentals of Visualization explores the design, development, and evaluation of information visualizations. Combine aspects of design, computer graphics, HCI, and data science, to gain hands-on experience with creating visualizations, using exploratory tools, and architecting data narratives. Topics include user-centered design, web-based visualization, data cognition and perception, and design evaluation. Cross listed with DTSA 5304.

CSCA 5063 Network Systems Foundation
Specialization: Network Systems: Principles and Practice (Linux and Cloud Networking)
Instructor: Eric Keller, Associate Professor

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In this course, students will learn the most important principles in network systems. This will center on the layered design of networks, and cover the link layer (Ethernet), network layer (IP), transport layer (TCP, UDP), and application layer (HTTP, gRPC). With those as a foundation, student will learn about network security problems and how some current solutions work at different layers.

CSCA 5073 Linux Networking
Specialization: Network Systems: Principles and Practice (Linux and Cloud Networking)
Instructor: Eric Keller, Associate Professor

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In this course students will learn how networking is designed and used in the Linux operating system. This will be learned in the context of networking principles and the application to real modern uses – building network operating systems (that power network appliances) and using Linux to support connectivity in modern containerized and virtualized applications (such as a Kubernetes network plugin)

CSCA 5083 Cloud Networking
Specialization: Network Systems: Principles and Practice (Linux and Cloud Networking)
Instructor: Eric Keller, Associate Professor

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In this class, students will learn about the networking abstractions and services for building applications in the cloud, and the technology underlying cloud networking. Students will be able to architect complex applications in the cloud. In understanding how the cloud providers created their networks, students will be in a better position to troubleshoot applications and analyze different possible ways of architecting applications, and even help design the next generation of networking for cloud providers.

CSCA 5834 Modeling of Autonomous Systems
Specialization: Foundations of Autonomous Systems
Instructor: Dr. Majid Zamani, Associate Professor, Co-Associate Chair for Graduate Education

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This course will explain the core structure in any autonomous system which includes sensors, actuators, and potentially communication networks. Then, it will cover different formal modeling frameworks used for autonomous systems including state-space representations (difference or differential equations), timed automata, hybrid automata, and in general transition systems. It will describe solutions and behaviors of systems and different interconnections between systems.

CSCA 5844 Requirement Specifications for Autonomous Systems
Specialization: Foundations of Autonomous Systems
Instructor: Dr. Majid Zamani, Associate Professor, Co-Associate Chair for Graduate Education

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This course will discuss different ways of formally modeling requirements of interest for autonomous systems. Examples of such requirements include stability, invariance, reachability, regular languages, omega-regular languages, and linear temporal logic properties. In addition, it will introduce non-deterministic finite and büchi automata for recognizing, respectively, regular languages and omega-regular languages.

CSCA 5854 Verification and Synthesis of Autonomous Systems
Specialization: Foundations of Autonomous Systems
Instructor: Dr. Majid Zamani, Associate Professor, Co-Associate Chair for Graduate Education

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This course will provide different techniques on the verification of autonomous systems against stability, regular, or omega-regular properties. Such techniques include Lyapunov theories, reachability analysis, barrier certificates, and model checking. Finally, it will introduce several techniques on designing controllers enforcing properties of interest over the original autonomous systems.

CSCA 5428 Object-Oriented Analysis and Design: Foundations and Concepts
Specialization: Object-Oriented Analysis & Design
Instructor: Bruce Montgomery, Senior Instructor, Faculty Director for Professional Master's Program

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An applied analysis and design class that addresses the use of object-oriented techniques. Topics include domain modeling, use cases, architectural design and modeling notations. Students apply techniques in analysis and design projects. Focus is on key object-oriented elements and concepts.

CSCA 5438 Object-Oriented Analysis and Design: Patterns and Principles
Specialization: Object-Oriented Analysis & Design
Instructor: Bruce Montgomery, Senior Instructor, Faculty Director for Professional Master's Program

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CSCA 5448 Object-Oriented Analysis and Design: Practice and Architecture
Specialization: Object-Oriented Analysis & Design
Instructor: Bruce Montgomery, Senior Instructor, Faculty Director for Professional Master's Program

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CSCA 5303 Attacking the Network
Specialization: Security and Ethical Hacking
Instructor: Ahmed Hamza, PhD., Associate Teaching Professor

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This course explains the science and art behind offensive security techniques used in penetration testing of networks and systems. A review of networking concepts is given. Students will utilize low-level programming through network interfaces, in executing a variety of network attacks, while learning to use essential auxiliary tooling. An introduction to cryptography for pentesters is provided.

CSCA 5313 Attacking Unix & Windows
Specialization: Security and Ethical Hacking
Instructor: Ahmed Hamza, PhD., Associate Teaching Professor

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This course in the sequence examines attacks on computer systems, with particular attention to Unix Security Model and Windows for memory corruption and binary exploitation. Students can expect to learn about, and apply offensive techniques against, Unix in general. We will demonstrate lateral movement and privilege escalation attacks, as well as buffer overflow and other memory exploitation primitives. Course assessments are through quizzes, hands-on exercises and an exam.

EMEA 5021 Product Cost and Investment Cash Flow Analysis
Specialization: Finance for Technical Managers
Instructor: Michael Readey, Professor of Engineering Practice