printer friendly pageAMSTUD 121L: Racial-Ethnic Politics in US (CSRE 121L, POLISCI 121L, PUBLPOL 121L)
This course examines various issues surrounding the role of race and ethnicity in the American political system. Specifically, this course will evaluate the development of racial group solidarity and the influence of race on public opinion, political behavior, the media, and in the criminal justice system. We will also examine the politics surrounding the Multiracial Movement and the development of racial identity and political attitudes in the 21st century.
Stats 60 or
Econ 1 is strongly recommended.
Last offered: Autumn 2013
| UG Reqs: GER:DB-SocSci, WAY-EDP, WAY-SI
BIOMEDIN 156: Economics of Health and Medical Care (BIOMEDIN 256, ECON 126, HRP 256)
Institutional, theoretical, and empirical analysis of the problems of health and medical care. Topics: demand for medical care and medical insurance; institutions in the health sector; economics of information applied to the market for health insurance and for health care; measurement and valuation of health; competition in health care delivery. Graduate students with research interests should take
ECON 249. Prerequisites:
ECON 50 and either
ECON 102A or
STATS 116 or the equivalent. Recommended:
ECON 51.
Terms: Aut, Win
| Units: 5
| UG Reqs: WAY-SI
Instructors:
Bhattacharya, J. (PI)
;
Dickstein, M. (PI)
BIOMEDIN 215: Data Driven Medicine
With the spread of electronic health records and increasingly low cost assays for patient molecular data, powerful data repositories with tremendous potential for biomedical research, clinical care and personalized medicine are being built. But these databases are large and difficult for any one specialist to analyze. To find the hidden associations within the full set of data, we introduce methods for data-mining at the internet scale, the handling of large-scale electronic medical records data for machine learning, methods in natural language processing and text-mining applied to medical records, methods for using ontologies for the annotation and indexing of unstructured content as well as semantic web technologies. Prerequisites:
CS 106A; familiarity with statistics (
STATS 202) and biology. Recommended: one of
CS 246 (previously
CS 345A),
STATS 305, or
CS 229.
Terms: Aut
| Units: 3
Instructors:
Shah, N. (PI)
BIOMEDIN 225: Data Driven Medicine: Lectures
With the spread of electronic health records and increasingly low cost assays for patient molecular data, powerful data repositories with tremendous potential for biomedical research, clinical care and personalized medicine are being built. But these databases are large and difficult for any one specialist to analyze. To find the hidden associations within the full set of data, we introduce methods for data-mining at the internet scale, the handling of large-scale electronic medical records data for machine learning, methods in natural language processing and text-mining applied to medical records, methods for using ontologies for the annotation and indexing of unstructured content as well as semantic web technologies. Prerequisites: familiarity with statistics (
STATS 202) and biology.
Terms: Aut
| Units: 2
Instructors:
Shah, N. (PI)
BIOMEDIN 256: Economics of Health and Medical Care (BIOMEDIN 156, ECON 126, HRP 256)
Institutional, theoretical, and empirical analysis of the problems of health and medical care. Topics: demand for medical care and medical insurance; institutions in the health sector; economics of information applied to the market for health insurance and for health care; measurement and valuation of health; competition in health care delivery. Graduate students with research interests should take
ECON 249. Prerequisites:
ECON 50 and either
ECON 102A or
STATS 116 or the equivalent. Recommended:
ECON 51.
Terms: Aut, Win
| Units: 5
Instructors:
Bhattacharya, J. (PI)
;
Dickstein, M. (PI)
CME 243: Financial Models and Statistical Methods in Active Risk Management (STATS 243)
(SCPD students register for 243P.) Market risk and credit risk, credit markets. Back testing, stress testing and Monte Carlo methods. Logistic regression, generalized linear models and generalized mixed models. Loan prepayment and default as competing risks. Survival and hazard functions, correlated default intensities, frailty and contagion. Risk surveillance, early warning and adaptive control methodologies. Banking and bank regulation, asset and liability management. Prerequisite:
STATS 240 or equivalent.
Terms: Win
| Units: 2-4
Instructors:
Lai, T. (PI)
COMM 106: Communication Research Methods (COMM 206)
(Graduate students register for
COMM 206.) Conceptual and practical concerns underlying commonly used quantitative approaches, including experimental, survey, content analysis, and field research in communication. Pre- or corequisite:
STATS 60 or consent of instructor.
Terms: Win
| Units: 5
| UG Reqs: GER:DB-SocSci
COMM 206: Communication Research Methods (COMM 106)
(Graduate students register for
COMM 206.) Conceptual and practical concerns underlying commonly used quantitative approaches, including experimental, survey, content analysis, and field research in communication. Pre- or corequisite:
STATS 60 or consent of instructor.
Terms: Win
| Units: 4
Instructors:
Voelker, D. (PI)
CS 161: Design and Analysis of Algorithms
Worst and average case analysis. Recurrences and asymptotics. Efficient algorithms for sorting, searching, and selection. Data structures: binary search trees, heaps, hash tables. Algorithm design techniques: divide-and-conquer, dynamic programming, greedy algorithms, amortized analysis, randomization. Algorithms for fundamental graph problems: minimum-cost spanning tree, connected components, topological sort, and shortest paths. Possible additional topics: network flow, string searching. Prerequisite: 103 or 103B; 109 or
STATS 116.
Terms: Aut, Spr, Sum
| Units: 3-5
| UG Reqs: GER:DB-EngrAppSci, WAY-FR
CS 229T: Statistical Learning Theory (STATS 231)
(Same as
STATS 231) How do we formalize what it means for an algorithm to learn from data? This course focuses on developing mathematical tools for answering this question. We will present various common learning algorithms and prove theoretical guarantees about them. Topics include online learning, kernel methods, generalization bounds (uniform convergence), and spectral methods. Prerequisites: A solid background in linear algebra and probability theory, statistics and machine learning (
STATS 315A or
CS 229). Convex optimization (
EE 364a) is helpful but not required.
Terms: Win
| Units: 3
Instructors:
Liang, P. (PI)
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