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 below. Overview: 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 modeling 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 Jupyter. Agenda: 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, under-fitting, 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 neighbors 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 a. Artificial Neural Networks b. Deep Learning c. Gaussian Processes d. Bayesian Networks e. Deep Reinforcement Learning Presenters: a. Prof. Bhushan Gopaluni, University of British Columbia, Canada b. Dr Aditya Tulsyan, Senior Engineer, Amgen Inc., USA c. Yiting Tsai, University of British Columbia, Canada d. Lee Repon, University of British Columbia, Canada Guest Speakers: a. Prof. Richard D. Braatz, Massachusetts Institute of Technology, USA b. Prof. Sirish L. Shah, University of Alberta, Canada Workshop Website: http://dais.chbe.ubc.ca/adchem Registration Website: http://adchem2018.org/Registration.html Please feel free to contact me if you require any additional information. Thanks Aditya Aditya Tulsyan, PhD., Senior Engineer, Digital Integration and Predictive Technologies, Amgen Inc., Email: [log in to unmask]