Programming Skills

• Languages: Python, Matlab, C
• Data packages & APIs: Pandas, Numpy, Matplotlib, Scikits-learn, Tensorflow, Keras, NLTK

Relevant Courseworks

Graduate level courseworks have been being officially enrolled during the Ph. D. study

• Winter 2017
  Optimization and Optimal Control
  McGill University
• Fall 2017
  Probabilistic Graphical Models
  MILA, University of Montreal
• Winter 2018
  Representation Learning - A Deep Learning Course
  MILA, University of Montreal

Supplemental Education

Self-conduct from Massive Open Online Courses (Coursera) & (EdX):

• Mathematics for Machine Learning Specialization given by Imperial College London:
  Mathematics for Machine Learning: Linear Algebra
  Mathematics for Machine Learning: Multivariate Calculus
  Mathematics for Machine Learning: PCA


• Probabilistic Graphical Models Specialization given by Stanford University:
  Probabilistic Graphical Models 1: Representation
  Probabilistic Graphical Models 2: Inference
  Probabilistic Graphical Models 3: Learning


• Natural Language Processing and Text Mining:
  Applied Text Mining in Python, University of Michigan
  Natural Language Processing, National Research University Higher School of Economics
  Natural Language Processing, Microsoft


• Neural Networks and Deep Learning:
  Bayesian Methods for Machine Learning, National Research University Higher School of Economics
  Applied Machine Learning in Python, University of Michigan
  Neural Networks for Machine Learning, University of Toronto
  Deep Learning Explained, Microsoft
  Neural Networks and Deep Learning, deeplearning.ai
  Improving Deep Neural Networks, deeplearning.ai