# Machine Learning

Notes for the Machine Learning unit.

## 📄️ Machine Learning Basics

What is Machine Learning?

## 📄️ Simple Linear Regression

Definition

## 📄️ Multiple Linear Regression

Definition

## 📄️ Practical 1

Use pandas to prepare and manipulate data. Use matplotlib to visualize data. Use statsmodel to train a linear regression model, and improve the model by analyzing its performance.

## 📄️ Overfitting

## 📄️ Bias & Variance

## 📄️ Regularization

## 📄️ Neural Network Components

This page covers the building blocks of neural network and convolutional neural network.

## 📄️ ResNets

References

## 📄️ Fine Tuning

Deep networks need a lot of data to train. What can you do when you don't have much?

## 📄️ Data Augmentation

Deep networks need lots of data. It's one of the more annoying things about them. Collecting data is very painful, and is one of the more annoying things about machine learning. Data augmentation is a partial solution to both these annoyances.

## 📄️ Dimension Reduction

## 📄️ Principal Component Analysis

References

## 📄️ Linear Discriminant Analysis

## 📄️ t-SNE

## 📄️ Siamese Networks

## 📄️ Contrastive Loss

## 📄️ Triplet Loss

## 📄️ Embedding Size

## 📄️ K-Means

notes about K-Means clustering.

## 📄️ Gaussian Mixture Models

A Gaussian mixture model assumes that each cluster has its own normal (or Gaussian) distribution with parameters 𝜇𝑐 and 𝜎𝑐.

## 📄️ Selection of K

How do we select the number of clusters for K-Means and GMMs?

## 📄️ HAC

Hierarchical Agglomerative Clustering

## 📄️ DBScan

Density-Based Spatial Clustering of Applications with Noise.

## 📄️ Evaluating Clustering Performance

Use Purity, Completeness, and V-Measure to evaluate clustering performance.

## 📄️ Diarisation

Group identities in media

## 📄️ Auto Encoders

Encoder-Decoder

## 📄️ Multi-Task Learning

Multiple outputs from a deep neural network.

## 📄️ Semi-Supervised Learning

When you cannot label all the data.

## 📄️ Variational Auto-Encoders

Learn distributions

## 📄️ Assignment 1A

- Name: Baorong Huang

## 📄️ Assignment 1B

48 hour extension

## 📄️ Assignment 1C

Problem 1. Clustering and Recommendations. Problem 2. Multi-Task Learning