Machine Learning

This repo contains all the lectures of Machine Learning Class at Universidad Tecnológica de Pereira.

Syllabus

• Introduction
  • What is Machine Learning ?
  • Introduction to Supervised Learning
  • Introduction to Un-Supervised Learning
• Linear Regression with one variable
  • Definition
  • Implementation
• Linear Regression with Multiple Variables
  • Multivariate Linear Regression
  • Normal Equation
  • Multivariate Linear Regression Implementation
  • Normal Equation Implementation
• Logistic Regression
  • Classification and Representation
  • Logistic Regression Model
  • MultiClass Classification
• Neural Networks: Representation
  • Motivations
  • Neural Networks
  • Applications
• Neural Networks: Learning
  • Cost Function and BackPropagation
  • Backpropagation in Practice
  • Application of Neural Networks
• Advice for Applying Machine Learning
  • Evaluating a Learning Algorithm
  • Bias vs Variance
• Machine Learning System Design
  • Building a Spam Classifier
  • Handling Skewed Data
  • Using Large Datasets
• Support Vector Machines
  • Large Margin Classification
  • Kernels
  • SVMs in Practice
• Unsupervised Learning
  • Clustering
• Dimensionality Reduction
  • Motivation
  • Principal Component Analysis
  • Applying PCA
• Anomaly Detection
  • Density Estimation
  • Building an Anomaly Detection System
  • Multivariate Gaussian Distribution
• Recommender Systems
  • Predicting Movie Ratings
  • Collaborative Filtering
  • Low Rank Matrix Factorization
• Large Scale Machine Learning
  • Gradient Descent with Large Datasets
  • Advanced Topics
    • Online Learning
    • Map Reduce and Data Parallelism
• Application Example

Note: This course is based on Coursera Machine Learning Course.