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00_timetable.md

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Semester timetable

Below are assignment due dates and a record of the topics we covered each week this semester, updated after each week. See syllabus for planned topics.

Assignment due dates

Assignment Due
1 (LOOCV) TBA
2 (classification, KNN) TBA
3 (decision trees, bagging) TBA
4 (boosting and neural networks) TBA
Individual project presentations (finals week, TBA) TBA
Individual project code or paper TBA

Discussions

Lead Week Day Date.... Paper
TBA TBA TBA TBA TBA

Week 1

  • Intro to class
  • What is machine learning?
  • Algorithms in data science: model, training, inference
  • Statistical inference: accuracy of a trained model
  • Regression and classification
  • Machine learning in a nutshell, all of it!

Week 2

  • Polynomial model algorithm
  • Optimization training algorithms
    • minimizing the training error
  • Cross-validation (CV) inference algorithm
    • k-fold CV for regression
    • train-test split
    • mean squared error
  • Tuning parameters

Week 3

  • Model algorithms
    • Smoothing splines
    • k nearest neighbors (KNN), regression and classification
  • Training algorithm
    • regularization: penalized least squares
  • CV inference algorithm
    • k-fold CV for classification
    • error rate
  • Theory: bias-variance tradeoff

Week 4

  • Model algorithms
    • Decision tree models
    • Ensemble algorithms: bagging
  • Training algorithms
    • Recursive binary partitioning for decision trees
  • Inference algorithms
    • Tuning decision trees with CV

Week 5

  • Model algorithms
    • Ensemble algorithms: bagging, random forests
  • Inference algorithms
    • Tuning bagging with CV
    • Tuning random forests with "out of bag" CV
    • Explainable machine learning: variable importance
  • Parallel processing
  • Using a compute server

Week 6

  • Model algorithms
    • Ensemble algorithm: boosted trees
  • Training algorithms
    • Gradient descent
    • Stochastic gradient descent
    • Gradient boosting
    • Stochastic gradient boosting
    • Extreme gradient boosting, XGBoost
  • Inference algorithms
    • Tuning strategies for multiple hyperparameters
  • One hot encoding for categorical variables

Week 7

  • Model algorithms
    • Single layer neural networks
    • Architectures for regression, classification, multifunction
    • Deep learning: multilayer neural networks
    • Wide vs deep, expressiveness
  • Training algorithm
    • Mini-batch stochastic gradient descent
    • Using Keras library

Week 8

  • Model algorithms
    • Deep learning: convolutional neural networks
  • Training algorithms
    • Dropout regularization
    • Train-validate-test split
  • Generalization & importance of data
    • Scope of inference
    • Test set leakage
    • Transfer learning, pre-trained models

Weeks 9-15

  • Reading and discussion: contemporary and emerging applications in ecology
  • Individual project