MLDS 2018: Machine Learning for Data Science
Predeal, Romania, August 2018

The students are expected to be comfortable with programming and to exhibit a basic level of mathematical dexterity (linear algebra, calculus, probability theory and statistics).

Lecture notes:
  1. Python, NumPy, SciPy, SciKit
  2. NumPy tutorial
  3. Linear Regression
  4. Gradient Descent
  5. Machine Learning: Logistic Regression, Softmax, SVMs
  6. Deep Learning: Fully Connected and Convolutional Neural Networks
  7. Deep Learning: Recurrent Neural Networks
  8. PyTorch tutorials

Evening talk:
  1. ML Applications

Hands-on exercises:
  1. Regression exercises
  2. Classification exercises

Project ideas:
  1. Time Series Prediction