printer friendly pageCS 325B: Data for Sustainable Development (EARTHSYS 162, EARTHSYS 262)
The sustainable development goals (SDGs) encompass many important aspects of human and ecosystem well-being that are traditionally difficult to measure. This project-based course will focus on ways to use inexpensive, unconventional data streams to measure outcomes relevant to SDGs, including poverty, hunger, health, governance, and economic activity. Students will apply machine learning techniques to various projects outlined at the beginning of the quarter. The main learning goals are to gain experience conducting and communicating original research. Prior knowledge of machine learning techniques, such as from
CS 221,
CS 229,
CS 231N,
STATS 202, or
STATS 216 is required. Open to both undergraduate and graduate students. Enrollment limited to 24. Students must apply for the class by filling out the form at
https://goo.gl/forms/9LSZF7lPkHadix5D3. A permission code will be given to admitted students to register for the class.
Terms: Aut
| Units: 3-5
| Repeatable
for credit
EARTHSYS 162: Data for Sustainable Development (CS 325B, EARTHSYS 262)
The sustainable development goals (SDGs) encompass many important aspects of human and ecosystem well-being that are traditionally difficult to measure. This project-based course will focus on ways to use inexpensive, unconventional data streams to measure outcomes relevant to SDGs, including poverty, hunger, health, governance, and economic activity. Students will apply machine learning techniques to various projects outlined at the beginning of the quarter. The main learning goals are to gain experience conducting and communicating original research. Prior knowledge of machine learning techniques, such as from
CS 221,
CS 229,
CS 231N,
STATS 202, or
STATS 216 is required. Open to both undergraduate and graduate students. Enrollment limited to 24. Students must apply for the class by filling out the form at
https://goo.gl/forms/9LSZF7lPkHadix5D3. A permission code will be given to admitted students to register for the class.
Terms: Aut
| Units: 3-5
| Repeatable
for credit
EARTHSYS 262: Data for Sustainable Development (CS 325B, EARTHSYS 162)
The sustainable development goals (SDGs) encompass many important aspects of human and ecosystem well-being that are traditionally difficult to measure. This project-based course will focus on ways to use inexpensive, unconventional data streams to measure outcomes relevant to SDGs, including poverty, hunger, health, governance, and economic activity. Students will apply machine learning techniques to various projects outlined at the beginning of the quarter. The main learning goals are to gain experience conducting and communicating original research. Prior knowledge of machine learning techniques, such as from
CS 221,
CS 229,
CS 231N,
STATS 202, or
STATS 216 is required. Open to both undergraduate and graduate students. Enrollment limited to 24. Students must apply for the class by filling out the form at
https://goo.gl/forms/9LSZF7lPkHadix5D3. A permission code will be given to admitted students to register for the class.
Terms: Aut
| Units: 3-5
| Repeatable
for credit
STATS 155: Statistical Methods in Computational Genetics
The computational methods necessary for the construction and evaluation of sequence alignments and phylogenies built from molecular data and genetic data such as micro-arrays and data base searches. How to formulate biological problems in an algorithmic decomposed form, and building blocks common to many problems such as Markovian models, multivariate analyses. Some software covered in labs (Python, Biopython, XGobi, MrBayes, HMMER, Probe). Prerequisites: knowledge of probability equivalent to
STATS 116,
STATS 202 and one class in computing at the
CS 106 level. Writing intensive course for undergraduates only. Instructor consent required. (WIM)
Terms: Aut
| Units: 3
Instructors:
Holmes, S. (PI)
STATS 202: Data Mining and Analysis
Data mining is used to discover patterns and relationships in data. Emphasis is on large complex data sets such as those in very large databases or through web mining. Topics: decision trees, association rules, clustering, case based methods, and data visualization. Prereqs: Introductory courses in statistics or probability (e.g.,
Stats 60), linear algebra (e.g.,
Math 51), and computer programming (e.g.,
CS 105).
Terms: Aut, Sum
| Units: 3
Instructors:
Taylor, J. (PI)
;
Tran, L. (PI)
;
Cai, F. (TA)
;
Feldman, M. (TA)
;
GAO, Z. (TA)
;
Guo, K. (TA)
;
Han, K. (TA)
;
Markovic, J. (TA)
;
Miao, J. (TA)
;
Qian, J. (TA)
;
Ray, S. (TA)
;
Rosenbaum, A. (TA)
;
Tirlea, M. (TA)
;
Wang, X. (TA)
;
Wu, H. (TA)
STATS 202U: Data Mining and Analysis
For Summer UG Visitors only. Sames as
Stats 202. This course is offered remotely only via video segments. TAs will host remote weekly office hours using an online platform such as Zoom.
Terms: Sum
| Units: 3
Instructors:
Tran, L. (PI)
;
GAO, Z. (TA)
;
Han, K. (TA)
;
Miao, J. (TA)
;
Rosenbaum, A. (TA)
;
Tirlea, M. (TA)
;
Wang, X. (TA)
STATS 216V: Introduction to Statistical Learning
Overview of supervised learning, with a focus on regression and classification methods. Syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines; Some unsupervised learning: principal components and clustering (k-means and hierarchical). Computing is done in R, through tutorial sessions and homework assignments. This math-light course is offered remotely only via video segments (MOOC style). TAs will host remote weekly office hours using an online platform such as Zoom. There are four homework assignments, a midterm, and a final exam, all of which are administered remotely. Prereqs: Introductory courses in statistics or probability (e.g.,
Stats 60 or
Stats 101), linear algebra (e.g.,
Math 51), and computer programming (e.g.,
CS 105). May not be taken for credit by students with credit in
STATS 202 or
STATS 216.
Terms: Sum
| Units: 3
STATS 245: Data, Models and Applications to Healthcare Analytics
Topics on fundamentals of data science, biological and statistical models, application to medical product safety evaluation, health risk models and their evaluation, benefit-risk assessment and multi-criteria decision analytics. Applications to environmental health, nutritional epidemiology, wellness and prevention will also be discussed. Prerequisite: Graduate students -
STATS 202 or 216, or
CS 229; Undergraduate students - consent of instructor.
Terms: Sum
| Units: 3
Instructors:
Choi, A. (PI)
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