Machine Learning Course

University of Alberta (MECE 610)

This course was developed by Armin Norouzi

Table of content:

• L01 Machine Learning Basics: Including hand-written and scikit-learn implementation
  • L01a Introduction_to-Machine_Learning: Linear regression, Logistic regression, and k-means algorithm
  • L01b Support Vector Machine for regression and classification
• L02 Artificial Neural Network Basics: Including hand-written and scikit-learn implementation
• L03 Deep Learning: Including theory and hand-written
• L04 Deep Learning with TensorFlow:
  • L04a Introduction to TensorFlow
  • L04b Convolutional Neural Network with TensorFlow
  • L04c Recurrent Neural Network with TensorFlow
  • L04d Natural Language Processing with TensorFlow
• L05 Deep Learning with PyTorch:
  • L05a Introduction to PyTorch
  • L05b Convolutional Neural Network with PyTorch
• L06 Transfer Learning with PyTorch and TensforFlow
  • L06a Transfer Learning with TensforFlow
  • L06b Transfer Learning with PyTorch
• L07 Generative AI
  • L07a Generative adversarial network with TensforFlow
  • L07bTransformer with TensforFlow

Notes:

• Developed with TensorFlow 2.8.2
• Developed with PyTorch 1.12.1+cu113
• Develop with Python 3.7.13 and compatible with Google Collaboratory