Get a head start on university computer science concepts
Advance your skills in programming and data analysis
The Computer Science Intermediate Track provides a unique combination of coding boot camp, and lab touring experiences, as well as UCLA coursework covering critical concepts and skills in computer programming related to statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, health data, geographical data, and social networks.
Computer science experience with basic programming skills (python) is required. Knowledge in basic matrix analysis, probability, and statistics is preferred.
Fees and Payment Info
The program fee includes the unit fees for the UCLA coursework offered as part of the program and thus varies by UC student status. In addition to the program fee, students are assessed other campus and administrative fees during the summer. This is a summary of fees that commonly apply to the selected student type.
Actual tuition and fees are subject to change by the University of California. Visit the fees, payment, and financial aid section for important disclaimer, as well as more details on fees, payment instructions, and information on delinquency, refunds, and financial aid.
Meet your instructors
Yizhou SunAssociate Professor
Yizhou Sun is an associate professor at the department of computer science at UCLA. She received her Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 2012. Her principal research interest is on mining graphs/networks, and more generally in data mining, machine learning, and network science, with a focus on modeling novel problems and proposing scalable algorithms for large-scale, real-world applications. She is a pioneer researcher in mining heterogeneous information network, with a recent focus on deep learning on graphs/networks.
Yizhou has over 100 publications in books, journals, and major conferences. Tutorials of her research have been given in many premier conferences. She received 2012 ACM SIGKDD Best Student Paper Award, 2013 ACM SIGKDD Doctoral Dissertation Award, 2020 ACM BCB Best Student Paper Award, 2013 Yahoo ACE (Academic Career Enhancement) Award, 2015 NSF CAREER Award, 2016 CS@ILLINOIS Distinguished Educator Award, 2018 Amazon Research Award, and 2019 Okawa Foundation Research Grant.
Dr. Gafni was born in Tel-Aviv, Israel. He received his Bs.C from the Technion, Israel in 1972, and M.S. and Ph.D. in Electrical Engineering in 1979 and 1982, from the University of Illinois at Urbana Champaign, and M.I.T, respectively. In 1982 he joined the UCLA computer science faculty. Dr. Gafni was the recipient of a 1983 IBM Faculty Development Award, and a 1984 NSF Presidential Young Investigator Award. His research interests include distributed algorithms, mathematical programming with application to distributed routing and control of data networks, and computer science theory.
Richard Korf is a Professor of computer science at UCLA. He received his B.S. from M.I.T. in 1977, and his M.S. and Ph.D. from Carnegie-Mellon University in 1980 and 1983, respectively, all in computer science. From 1983 to 1985, he served as Herbert M. Singer Assistant Professor of Computer Science at Columbia University. His research is in the areas of problem-solving, heuristic search, and planning in artificial intelligence. He is the author of “Learning to Solve Problems by Searching for Macro-Operators” (Pitman, 1985).
He serves on the editorial boards of Artificial Intelligence, and the Journal of Applied Intelligence. Dr. Korf is the recipient of a 1985 IBM Faculty Development Award, a 1986 NSF Presidential Young Investigator Award, the first UCLA Computer Science Department Distinguished Teaching Award in 1989, the first UCLA School of Engineering Student’s Choice Award for Excellence in Teaching in 1996, and the Lockheed Martin Excellence in Teaching Award in 2005. He is a Fellow of the American Association for Artificial Intelligence.