printer friendly pageCEE 154: Data Analytics for Physical Systems (CEE 254)
This course introduces practical applications of data analytics and machine learning from understanding sensor data to extracting information and decision making in the context of sensed physical systems. Many civil engineering applications involve complex physical systems, such as buildings, transportation, and infrastructure systems, which are integral to urban systems and human activities. Emerging data science techniques and rapidly growing data about these systems have enabled us to better understand them and make informed decisions. In this course, students will work with real-world data to learn about challenges in analyzing data, applications of statistical analysis and machine learning techniques using MATLAB, and limitations of the outcomes in domain-specific contexts. Topics include data visualization, noise cleansing, frequency domain analysis, forward and inverse modeling, feature extraction, machine learning, and error analysis. Prerequisites:
CS106A,
CME 100/
Math51,
Stats110/101, or equivalent.
Terms: Aut
| Units: 3-4
CEE 254: Data Analytics for Physical Systems (CEE 154)
This course introduces practical applications of data analytics and machine learning from understanding sensor data to extracting information and decision making in the context of sensed physical systems. Many civil engineering applications involve complex physical systems, such as buildings, transportation, and infrastructure systems, which are integral to urban systems and human activities. Emerging data science techniques and rapidly growing data about these systems have enabled us to better understand them and make informed decisions. In this course, students will work with real-world data to learn about challenges in analyzing data, applications of statistical analysis and machine learning techniques using MATLAB, and limitations of the outcomes in domain-specific contexts. Topics include data visualization, noise cleansing, frequency domain analysis, forward and inverse modeling, feature extraction, machine learning, and error analysis. Prerequisites:
CS106A,
CME 100/
Math51,
Stats110/101, or equivalent.
Terms: Aut
| Units: 3-4
STATS 110: Introduction to Statistics for Engineering and the Sciences
Introduction to statistics with examples drawn from various fields, including the sciences, engineering, and social sciences. Collecting data (random sampling, randomized experiments); describing data (numerical and graphical summaries); discrete and continuous probability models; statistical inference (hypothesis tests and confidence intervals). Use of software to conduct probability simulations and data analysis. Prerequisite:
MATH 20 or AP Calculus AB.
Terms: Aut, Sum
| Units: 5
| UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR
Instructors:
Smith, H. (PI)
;
Sun, D. (PI)
;
Feldmeier, R. (TA)
...
more instructors for STATS 110 »
Instructors:
Smith, H. (PI)
;
Sun, D. (PI)
;
Feldmeier, R. (TA)
;
Katiyar, E. (TA)
;
Lee, J. (TA)
;
Sudijono, T. (TA)
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