Ctrl+K

Notations

Optimization

Machine Learning

The Machine Learning Framework
Fundamentals
Linear Models
- Logistic Regression
- Generalized Linear Models
Model Selection and Evaluation
- Metrics and Scoring Rules
  - Classification Metrics
    
    Accuracy
    
    Precision, Recall and F1 Score
    
    Brier Score
  - Regression Metrics
    
    Mean Absolute Error
    
    (Root) Mean Squared Error
    
    Mean Absolute Percentage Error
- Learning Curve Theory
Trees, Forests, Bagging and Boosting
- Decision Trees
  - Braindump
- Ensemble Learning
Dimensionality Reduction
- Principal Component Analysis
  - Concept
  - PCA
  - Eigenface
Neighbourhood
- K-Nearest Neighbours

Deep Learning

Numerical Stability and Initialization
Computer Vision
- Modern Convolutional Neural Networks
  - Residual Networks
    
    Concept
Natural Language Processing Specialization
- Natural Language Processing with Classification and Vector Spaces
  - Sentiment Analysis with Logistic Regression
    
    Preprocessing
    
    Building and Visualizing word frequencies
    
    Visualizing tweets and the Logistic Regression model
Natural Language Processing (NLP)
- Makemore
- Vector Semantics and Embeddings

References, Resources and Roadmap

Loss

Loss#

Read 2.3 of Machine Learning: The Basics book. Also maybe empirical risk minimization.?

By Gao Hongnan