Advanced Machine Learning Tools for Engineers

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UBC Calendar

Credits

EECE 571T

Course Description

  • This course provides an introduction to essentials of machine learning, deep learning algorithms and tools, and their applications for engineers and engineering problems.
  • This course has no graduate prerequisites or co-requisites. Knowledge of probability, statistics, and programming is required for this course.
  • Course cooordinators: Dr. Purang Abolmaesumi (EECE) and Dr. Bhushan Gopaluni (CHBE).
  • Course lecturers: Lee Rippon (CHBE), Delaram Behnami (EECE), Fatemeh Taheri Dezaki (EECE), Mohammad Hossein Jafari (EECE).

Course Topics

1. Clustering and dimensionality reduction algorithms

  • K-nearest neighbours
  • Principal Component Analysis (PCA)
  • Partial Least Squares (PLS)
  • Discriminant Analysis
  • Isometric Mapping (ISOMAP)
  • Local Linear Embedding (LLE)
  • Multidimensional Scaling (MDS)

2. Classification and regression algorithms

  • K-means algorithm
  • Support vector machines
  • Naive Bayes classifier and decision trees
  • Hierarchial clustering
  • Random forests
  • Linear and nonlinear least squares
  • Logistic regression
  • Kernels and regularization

3. Introduction to deep learning

  • Neurons, neural networks, multi-layer perceptron (MLP)
  • Convex optimization, stochastic gradient decsent (SGD)
  • Backpropogation
  • Transfer learning, augmentation, regularization
  • Introduction to Tensorflow, Keras, Google Colab

4. Deep learning in images and time series

  • Convolutional neural networks (CNNs)
  • Applications of CNNs: image classification, object/abnormality detection, segmentation, attention models, etc.
  • Recurrent Neural Networks (RNNs) Long-short Term Memory (LSTM), Gated Recurrent Unit (GRU)
  • Applications of RNNs: forecasting, time series and video analysis, natural language processing (NLP)

5. Advanced machine learning algorithms

  • Generative Adversarial Networks (GANs)
  • Variational Auto-Encoders (VAEs)
  • Active learning, online learning
  • Self-supervised learning, semi-supervised learning
  • Label propagation, weak labelling
  • Reinforcement learning

Course Assessment

  • Assignments 40%; four assignments in total.
  • Course project: 60%; project proposal, oral presentation, written report
Professor: 

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