/week-4-Intro-to-Machine-Learning-1-for-Classification-Logistic-Regression-and-SVM

Intro to Machine Learning for Classification: Logistic Regression and SVM

Primary LanguageJupyter Notebook

Intro to Machine Learning for Classification 1: Logistic Regression and SVM

Part one: class organization

  1. Finish research proposal in Github as a readme file: a. what is your research question? b. why your dataset can answer the question?
  2. All students read out their reserach proposals

Part two: programming

Introduction to Machine Learning for Classification

Logistic Regression

  1. Do we really understand the Log Loss Calculation in Logistic Regression? Instead of Mean Squared Error for Linear Regression, we use a cost function called Cross-Entropy, also known as Log Loss for Logistic regression.
  2. Binary Classification
  3. One vs. Rest: Multiple Categories Classification

SVM:

Explain how SVM works.

Part three: project management

  1. Upload new files into github (reference papers, data & codes)
  2. Start HW2: Data collection, clearning and exploration

Reference:

  1. https://ml-cheatsheet.readthedocs.io/en/latest/logistic_regression.html