CAST10 Archives

April 2018


Options: Use Monospaced Font
Show Text Part by Default
Show All Mail Headers

Message: [<< First] [< Prev] [Next >] [Last >>]
Topic: [<< First] [< Prev] [Next >] [Last >>]
Author: [<< First] [< Prev] [Next >] [Last >>]

Print Reply
Aditya Tulsyan <[log in to unmask]>
Reply To:
Aditya Tulsyan <[log in to unmask]>
Thu, 12 Apr 2018 09:50:46 -0400
text/plain (91 lines)
Dear Colleagues, 

We are organizing a one-day workshop on  "Advanced Tools for Process Data Analytics" 
at the 10th IFAC Symposium on Advanced Control of Chemical Processes (ADCHEM 2018) 
in Shenyang, China from July 25-27, 2018. The workshop overview and agenda are given 

We are currently at the cusp of what is considered the fourth industrial revolution. This 
revolution is driven by the ubiquitous cyber-physical systems, algorithmic developments 
in artificial intelligence, gargantuan computing power, inexpensive memory and the 
gigantic volumes of data that are being collected. The process industries are in 
possession of treasure troves of heterogeneous data that is gravely underutilized. The 
competitive global environment and the ever-increasing demands on energy, environment 
and quality are subjecting these industries to a high level of economic pressure. The 
incredible volumes of data that they already possess are poised to provide a level of 
automation and efficiency never seen before and thus alleviate the economic and 
competitive pressures.

Process industries have been using data analytics in various forms for more than three 
decades. In particular, statistical techniques such as principal component analysis (PCA), 
partial least squares (PLS), canonical variate analysis (CVA) and time series methods for 
modelling such as maximum likelihood estimation, prediction error methods have been 
extensively applied on industrial data. The recent developments in machine learning and 
artificial intelligence provide a new opening for using process data on large scale 
problems. However, in order to successfully apply machine learning methods to process 
data, researchers require not only a high-level understanding of the algorithms but also 
strong programming knowledge in packages such as Python, TensorFlow, Keras and 


Starting with an elementary introduction to statistics and probability, we will develop 
various regression, classification, dimensionality reduction and advanced learning 
algorithms that are of interest to engineers. In addition, various widely-used machine 
learning software packages will be introduced. Registrants will solve exercises and 
receive takeaway software code to implement these algorithms. The course will start with 
basics of probability and statistics, underfitting, overfitting and bias-variance tradeoff. 
After that, the course will consist of the following:

1.  Big Data Analytics (Guest Lecture 1)

2.  Classification Algorithms
a. k-nearest neighbours algorithm
b. k-means algorithm
c. Support Vector Machines
d. Naive Bayes Classifier and Decision Trees

3. Regression Algorithms
a. Linear Least Squares
b. Non-linear Least Squares
c. Kernel Regression

4. Dimensionality Manipulation Algorithms
a. Principal Component Analysis (PCA)
b. Principal Least Squares (PLS)
c. Isometric Mapping (ISOMAP)
d. Local Linear Embedding (LLE)
e. Canonical Correlation Analysis
f. Multidimensional Scaling (MDS)

5. Alarm Management (Guest Lecture 2)

6. Advanced Learning Algorithms
1. Artificial Neural Networks
2. Deep Learning
3. Gaussian Processes
4. Bayesian Networks
5. Deep Reinforcement Learning

1. Prof.  Bhushan Gopaluni, University of British Columbia, Canada
2. Dr Aditya Tulsyan, Senior Engineer, Amgen Inc., USA
3. Yiting Tsai, University of British Columbia, Canada
4. Lee Repon, University of British Columbia, Canada

Guest Speakers: 
1. Prof. Richard D. Braatz, Massachusetts Institute of Technology, USA
2. Prof. Sirish L. Shah, University of Alberta, Canada

Workshop Website:
Registration Website:

Please feel free to contact me (email: [log in to unmask]), if you require any 
additional; information.