Data Visualization, Information Design, Interactive Dashboards, Interpretability
This course taught me how to transform complex data into clear and intuitive visualizations to support analysis and decision-making. I learned principles of chart design, effective use of color and layout, and how to build interactive dashboards that enhance the expressiveness and interpretability of data. Through practical projects, I completed several visualization cases independently and gained insights into designing user-friendly, purpose-driven data display systems for various user needs.
COVID-19 Visualization Dashboard
Bayesian Learning, Regression Analysis, Graphical Models, Probabilistic Inference, Advanced Machine Learning
This course provided an in-depth study of probabilistic methods and model inference in machine learning, focusing on Bayesian learning, regression analysis (both linear and nonlinear), and graphical models such as Bayesian networks and Markov random fields. Through rigorous mathematical derivation, I developed an understanding of the structure, parameter estimation, and real-world application of these models. The course emphasized theoretical depth and abstract modeling, equipping me with the ability to design and interpret machine learning systems from a probabilistic perspective.