1

Smartphone sensing data

Technologies, data lifecycle, Preparing the data. Understanding the data. Modeling and evaluation. Actionable data driven models.

2

Data driven methods for smartphone sensing problems

Supervised learning, Unsupervised learning, Reinforcement learning. Theory-based Data Science Models.

3

Smartphone based analytics

Example I: Harsh event detection in R.

4

Understanding and modeling smartphone sensing data I

Example II: context aware detection problems in R.

5

Understanding and modeling smartphone sensing data II

Example III: User profiling in R.

6

From understanding to decision making

Example IV: Policy making with Reinforcement Learning, an application in R.

7

Simulation

Categories of simulation, Stochastic simulation, random numbers, Monte Carlo Simulation. Simple examples

8

Data extraction 

Data mining, autocorrelation, cross-correlation, power Spectrum. Discrete and continuous stochastic processes. Sampling and times series. White noise. Fourier transforms. Computational aspects of power spectrum. Analysis of large scale geophysical time series

9

Data driven models

Spectral representation and Karhunen–Loève series expansion methods. Simulation of stationary stochastic processes. Markov models

10

Data driven models

Reduced order models: Neural network, Support Vector Machines, Gaussian Processes (GPR, GPC).

11

Machine learning for structural performance assessment -1

Example 1: nonlinear response history analysis of a simple structural system

Example 2:  a rocking system

12

Machine learning for structural performance assessment - 2

Stochastic and Reliability analysis

Optimization under uncertainty.

13

Applications

Example: Reliability analysis of a simple structural system