Notes for the Machine Learning unit.
📄️ Machine Learning Basics
What is Machine Learning?
📄️ Simple Linear Regression
📄️ Multiple Linear Regression
📄️ 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.
📄️ Bias & Variance
📄️ Neural Network Components
This page covers the building blocks of neural network and convolutional neural network.
📄️ 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
📄️ Linear Discriminant Analysis
📄️ Siamese Networks
📄️ Contrastive Loss
📄️ Triplet Loss
📄️ Embedding Size
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?
Hierarchical Agglomerative Clustering
Density-Based Spatial Clustering of Applications with Noise.
📄️ Evaluating Clustering Performance
Use Purity, Completeness, and V-Measure to evaluate clustering performance.
Group identities in media
📄️ Auto Encoders
📄️ Multi-Task Learning
Multiple outputs from a deep neural network.
📄️ Semi-Supervised Learning
When you cannot label all the data.
📄️ Variational Auto-Encoders
📄️ Assignment 1A
- Name: Baorong Huang
📄️ Assignment 1B
48 hour extension
📄️ Assignment 1C
Problem 1. Clustering and Recommendations. Problem 2. Multi-Task Learning