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April 2018


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Aditya Tulsyan <[log in to unmask]>
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Aditya Tulsyan <[log in to unmask]>
Thu, 12 Apr 2018 09:50:46 -0400
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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.