10-13 July 2017
Princeton University
US/Eastern timezone

Machine Learning Methods

12 Jul 2017, 13:30
1h 30m
407 Jadwin Hall (Princeton University)

407 Jadwin Hall

Princeton University

Princeton Center For Theoretical Science (PCTS)


Alexey Svyatkovskiy (Princeton University)


This session will cover the basics of machine learning and deep learning. We will discuss basic learning algorithms; overfitting and regularization; hyper parameter search (grid search, random search) and cross validation (stratified, k-fold); bias, variance trade-off and learning curves.

  • Supervised learning: decision trees and random forests, bootstrap aggregation and boosting;
    deep feed forward neural networks, forward propagation, back propagation, dropout regularization, Stochastic Gradient Descent; why training on mini-batches; brief introduction to convolution networks
  • Unsupervised learning: k-means clustering; locality sensitive hashing families, MinHash, Jaccard similarity. Case study: natural language processing

Bonus: deep recurrent neural networks, unfolding through time, BPTT; LSTM. Case study: analyzing time series data of variable length.

Presentation Materials