This course presents the astronomical phenomena of our Milky Way Galaxy. We examined the physical processes that govern the stars, planets, black holes, and other objects in our galaxy, and we learned about how scientists work to understand these processes. This class is firmly rooted in the basic underlying principles of physics and mathematics. Final … Continue reading “ASTR 15: Stars and the Milky Way”
Campus Involvement COSO is made up of about 20 students who are responsible for recognizing new groups, making funding decisions, and supporting a diverse and wide range of groups, activities, and events. We meet once a week for about 1.5 hours to listen to proposals and allocate funds in the best interest of Dartmouth undergraduates. I … Continue reading “Commitments @Dartmouth”
Description What makes a great leader? Why would others follow you? A century of psychological research clarifies the traits, skills and behaviors of effective leaders, including how they attract, influence, develop and empower followers to excel in different situations. Readings will explain the psychological science; classroom demonstrations, guest leader interviews, case study discussions and small … Continue reading “PSYC 54.08: Leadership”
Description An introduction to the field of Artificial Intelligence. Topics include games, robotics, motion planning, knowledge representation, logic and theorem proving, probabilistic reasoning over time, understanding of natural languages, and discussions of human intelligence. Homework
Here is the page where you get to know more about me: more than just a Dartmouth student, a Computer Science major, or an intern. Where am I from? I was born and raised in Shanghai, China. I believe it is one of the most amazing cities in the word. To summarize Shanghai, it is … Continue reading “But really, who am I?”
Description This course provides an introduction to statistical modeling and machine learning. Topics include learning theory, supervised and unsupervised machine learning, statistical inference and prediction, and data mining. Applications of these techniques to a wide variety of data sets will be described. Homework Demo https://github.com/sixuanhan/CS74-machine-learning
Description This course introduces core computational and mathematical techniques for data analysis and physical modeling, foundational to applications including computational biology, computer vision, graphics, machine learning, and robotics. The approaches covered include modeling and optimizing both linear and nonlinear systems, representing and computing with uncertainty, analyzing multi-dimensional data, and sampling from complex domains. The techniques are both grounded in mathematical … Continue reading “COSC 70: Foundations of Applied CS”
Description The architecture and organization of a simple computer system is studied. Topics covered include how information is represented in memory, machine-language instructions and how they can be implemented at the digital logic level and microcode level, assembly language programming, and input/output operations. Speedup techniques, such as pipelining and caching, are also covered. Homework Demo … Continue reading “COSC 51: Computer Architecture”