MLtutorials

Lab internal ML teaching tutorials & Markdown slides for Google Colab to get hands-on experience using PyTorch.
Based on Goodfellow et al. (2015) "Deep Learning"

Pre-requisites

• Basic knowledge of Python - see this course
• Basics of Linear Algebra, and Probabilities / Statistics
• see also Numpy for Matlab users

Syllabus

Week 0

Presenter: Gunnar

• Using Google Colab
• how to make Mardown slides with Jupyter Notebook
  Notebook:
  

Week 1

Presenter: Matt

• Learning algorithms

• Capacity, overfitting, underfitting

• Hyperparameters & validation sets

• Estimators, bias, variance

  Notebook:
  

Week 2

Presenter: Ben

• Linear regression

• Cost functions

• Maximum likelihood estimation

  Participant notebook:
  

  With solutions:
  

Week 3

Presenter: Ben

• MLE for logistic regression

  Participant notebook:
  

  With solutions:
  

Week 4

Presenter: Matt

• Gradient descent

  Participant notebook:
  

  With solutions:
  

Week 5

Presenter: Gunnar

• Error Backpropagation

  Participant notebook:
  

  With solutions:
  

Week 6

Presenter: Ben

• Intro to PyTorch

  Participant notebook:
  

  With solutions:
  

Week 7

Presenter: Ben

• Recurrent neural networks

  Part 1:
  

  Part 2:
  

pandas + seaborn

Presenter: Ben