Milad Memarzadeh
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CE 88 - Data Science for Smart Cities

Fall 2017 Data Science Connector Course
Instructor: Milad Memarzadeh, GSI: Sangjae Bae
Connector assistants: Robert Blanco and Mohammed Hossain

Location & time: Monday 12:00-2:00 pm, 406 Davis Hall

Office Hours: 
Robert, Mondays 2-4pm, at
458 Evans Hall
Milad, Wednesdays 12-2pm at 537 Davis Hall
Sangjae and Mohammed, Fridays 10am-12pm at 537 Davis Hall

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Cities are becoming more dependent on data flows that connect users and infrastructure. Design and operation of smart, efficient, and resilient cities has come to require data science skills. Today, rich data and powerful algorithms in the hands of domain experts have transformed decisions about marketing and advertising, but not those decisions about how we maintain our urban infrastructure and built environment. It is time to understand how these approaches can be applied to the challenges in adaptive monitoring and control of civil infrastructure systems, and to invent the tools which will allow researchers and practitioners to better leverage these approaches so that decision-making will be based on all  available data. This course teaches you the fundamentals of reproducible data science and analytics, probability and statistics, and machine learning to leverage data generated within transportation systems, environmental systems, building energy systems, and power grids, via crowd-sensing, and remote sensing technologies.

The Data Science for Smart Cities connector is offered through the Civil and Environmental Engineering Department at UC Berkeley and is taught in conjuction with the Foundations of Data Science course. The Foundations of Data Science course provides a baseline of computing skills, data visualization, and statistical concepts. There are no formal pre-requisites so you can also take it independently of Data8 (background of junior/senior standing in any engineering major helps). Don't forget to enroll in all 3 components (Lecture, Lab, Discussion).

Syllabus

Course will be taught in four modules: (1) Mobility & Transportation, (2) Building Energy Systems, (3) Extreme Events & Urban Resilience, and (4) Climate Change & Environmental Variability. 

Date
Topic
Reading
Lecture
Assignment
Lab
Aug 28
Introduction to smart cities
   
Lec0   
   ​
Lab   
Sep 11
Module 1: Data visualization special lecture: Calthorpe Analytics
   ​
Lec01
   ​
​Lab01  ​
Sep 18
Module 1: Linear algebra
Ref02   ​
  Lec02 ​
   ​
Lab02   ​
Sep 25
Module 1: Statistics, probability and uncertainty
 Ref03  ​
  Lec03   ​
HW01   ​
 Lab03  ​
Oct 2
Module 2: Linear regression
 Ref04  ​
  Lec04   ​​
   ​
 Lab04  ​
Oct 9
Module 2: Bayesian vs. Data-driven
Ref05
 Lec05  ​
HW01 Solution   
  Lab05 ​
Oct 16
Module 2: Bayesian networks: Causality
 Ref06  ​
  Lec06   ​
 HW02  ​
 Lab06  ​
Oct 23
Module 3: Dynamical systems: Markov chains
Ref07_1
 Ref07_2 ​
Lec07​
HW02
Solution
 
 ​
Lab07   ​
Oct 30
Module 3: Loss and decisions
 Ref08  ​
 Lec08  ​
HW03 
Lab08​
Nov 6
Module 3: Sequential decision making
   ​
   ​
   HW03    
 Solution
  ​
Lab09   ​
Nov 13
Module 4: Supervised learning: k-nearest neighbour
  Ref10 ​
 Lec10  ​
 HW04  ​
 Lab10  ​
Nov 20
Module 4: Climate change special lecture: Mavrx
   ​
 Lec11  ​
   ​
 Lab11   ​
Nov 27
Module 4: Unsupervised learning: clustering with k-means
  Ref12 ​
 Lec12  ​
HW04
   Solution 
 
​
 Lab12   ​
Dec 4
Wrap-up & Final project presentations
   ​
   ​
 Project  ​
   ​
Dec 11
Final project presentations
   ​
   ​
   ​
   ​
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  • Media