Harvard CS 205
Mondays and Wednesdays 2:30-4:00pm, Pierce Hall 209
Instructor: Professor H.T. Kung
Computational science grows in popularity each day. Via parallel and distributed computing,
advances in the area have enabled practitioners in diverse fields, including physical sciences,
biotechnology, medicine, finance, and engineering, to discover and recognize principles and
patterns in data. To understand, harness, and further these powerful capabilities, students must
grasp both relevant computer science foundations and programming skills. To this end, this
course consists of foundational modules and programming tasks essential to the theory and
practice of data science:
Data-driven model learning
Parallel computing, GPU
Distributed computing, MapReduce
Neuromorphic computing for asynchronous data flows
IO complexity and management
Compressive sensing and dimension reduction
Optimistic concurrency control
Distributed machine learning
The course instruction has two components: lectures and labs. Lectures will focus on teaching
the foundational modules based on research literature. The labs will provide assistance on the
programming tasks, and will use server clusters at Harvard as well as remote resources in the
cloud. In addition, labs will have access to state-of-the-art 3D cameras for data acquisition.
Students will learn to use open source tools and libraries and apply them to data analysis,
modeling, and the visualization of machine learning and scientific computing problems.
Students will complete weekly quizzes on assigned reading materials, practice skills through
programming tasks, and implement a final project (in 3- or 4-person teams) using concepts and
skills learned in the course.
Prerequisites: 1) programming experience (Python and CS50 should be fine); (2) basic
knowledge in systems programming and machine organization (e.g., CS61);
(3) familiarity in algorithms (e.g., CS124); and (4) maturity in mathematics (e.g., undergraduate linear algebra and
statistics). For students with strong interest in the subject matter, one or two of these four
requirements may be waived.