Trees, SVM and Unsupervised Learning

The University of Colorado Boulder, founded in 1876 and set against the Rocky Mountains, is a major research university serving more than 37,000 students with nearly 3,000 international learners. CU Boulder is recognized for academic excellence, expansive research programs and a sustainability-driven campus culture. Students benefit from an array of undergraduate, graduate and professional offerings that encourage innovation across science, engineering, business, arts and humanities.

Research, entrepreneurship and experiential learning are central to the CU Boulder experience, with extensive laboratory facilities, innovation labs and co-op or internship pathways that connect students to industry and civic partners. The university supports cross-disciplinary projects and provides robust advising, career services and student organizations that help translate academic interests into practical outcomes and start-up ventures.

Boulder's outdoor lifestyle and proximity to tech hubs create strong opportunities for networking, recreation and balanced living. International students are supported by dedicated offices for admissions, orientation and visa guidance, making the transition smoother. With a large program portfolio and a campus culture that values sustainability, creativity and collaboration, CU Boulder is well suited to students seeking high-impact research experiences and an active campus community.

The Trees, SVM and Unsupervised Learning program is a non-degree course that teaches you about support vector machines, neural networks, and decision trees. It's a short online program that lasts a few weeks and is taught in English. You'll learn how to build powerful predictive models and understand the advantages and disadvantages of each technique.

In this program, you'll gain practical hands-on experience with techniques like PCA and clustering. You'll learn how to generate data representations and apply these techniques to different scenarios, including binary classification and multi-class problems. The program focuses on practical, real-world applications and helps you develop valuable skills in data science.

After completing this program, you can pursue careers like Data Scientist, Business Analyst, or Machine Learning Engineer. You can work in industries like finance, healthcare, or technology. Some potential employers include companies like Google, Microsoft, or IBM. You'll have the skills to work with predictive models and apply data science techniques to real-world problems.