/intro2ml_notes

Notes for the Introduction to Machine Learning course UniTN

Primary LanguageTeX

A short introduction to Machine Learning

This repository contains my personal notes about the course Introduction to Machine Learning of University of Trento.

Contents

Part One: Basics

  • Introduction
  • Data, Features, Models
  • Generalization error, Models, Hypothesis space

Part Two: Supervised Learning

  • k-Nearest Neighbor
  • Linear Models
  • Decision Trees
  • Multiclass Classification
  • Gradient Descent
  • Regularization
  • Support Vector Machines
  • Ranking
  • Neural Networks

Part Three: Unsupervised Learning

  • Unsupervised Learning
  • Clustering
  • Deep Generative Models

Part Four: Reinforcement Learning

  • Reinforcement Learning

Structure

All the utils of the project are collected into the principal file, called ml.pdf. The folders are organized as follows:

  • chapters contains the .tex file for each chapter.
    • In each chapter there is a final part that contains exercises, open questions, the questions that contains the '!' symbol are the ones that have been asked in the previous exams.
  • img contains all the images files of the project.

Usage

In order to compile the latex document into your machine, clone the repository, go to the the clone directory and run the command

pdflatex ml

Error reports

In order to report some errors in the notes, please open an issue specifying the chapter, the section, and the kind of error; or provide your own correction in a new pull request.