University of California
Department of Civil Engineering
Prof Alexei Pozdnukhov
CE 263N Scalable Spatial Analytics
Sample Syllabus
Prerequisites
No formal prerequisites. (an undergraduate degree in engineering domain is expected, with
knowledge of linear algebra and statistics, as well as some programming experience)
Primary goal: Provide theoretical background and hands-on experience in predictive modelling
for designing scalable data-driven systems and location-based services based on data analytics.
Short Description: Introduction to modern methods of data analysis, spatial data handling and
visualization technologies for engineers and data scientists. Theoretical coverage includes a
selection of methods from spatial statistics, exploratory data analysis, spatial data mining,
discriminative and generative approaches of machine learning. Projects and assignment tasks are
targeted at real-world scalable implementation of systems and services based on data analytics in
environmental remote sensing, transportation, energy, location-based services and the domain of
“smart cities” in general.
Instructor: Prof Alexey Pozdnukhov
Email:
Phone:
Office:
alexeip@berkeley.edu
(510) 642-1008
115 McLaughlin Hall
Course Reader
The course readings are available on bCourses as pdf files and/or links to online materials.
Course Grading
1 or 2 Midterms
Homework (6 problems)
Final Project
CE 263 Scalable Spatial Analytics
30%
50%
20%
University of California
Department of Civil Engineering
Prof Alexei Pozdnukhov
Course Schedule
LECTURE
DATE TOPIC
THEME
1
2
3
4
Introduction
Exploratory data analysis
Clustering
Dimensionality reduction
General
EDA
EDA
EDA
5
6
7
Spatial databases I
Spatial databases II
GIS and web mapping
Data Handling
Data Handling
Data Handling
8
9
Trajectories and map matching
Map matching and reconstruction
Data Processing
Data Processing
10
11
12
13
Point Pattern Analysis
Geostatistics
Spatial Regression
Geographically weighted models
Spatial Statistics
Spatial Statistics
Spatial Statistics
Spatial Statistics
14
15
16
17
18
Intro to ML, model selection
Locally linear models
Kernel methods I, SVM
Kernel methods II, SVR
Elements of computer vision
ML discriminative
ML discriminative
ML discriminative
ML discriminative
ML discriminative
19
20
21
22
Mixture models, EM
MC, HMM, CRF
Spatial PLSA/LDA
Recommender Systems
ML generative
ML generative
ML generative
ML generative
23
24
25
26
27
Complex networks
Agent-based models
Catch-up
Final projects
Final projects
Complex Systems
Complex Systems
General
General
General
CE 263 Scalable Spatial Analytics
PROBLEMS
DUE