This is a study guide for understanding machine learning and gpt3]]

Built from the GPT3 prompts in machine learning study guide gpt

Over time I will make notes on these subjects and link to them here.

## Mathematics

### Linear algebra

#### Vector Spaces

#### Matrices

#### Determinants

#### Eigenvectors and Eigenvalues

#### Systems of Linear Equations

#### Vector operations

##### Addition

##### Subtraction

##### Scalar multiplication

##### Dot product

##### Cross product

#### Linear independence and span

#### Orthogonality

#### Projections

#### Eigendecomposition

#### Singular value decomposition

#### Principal component analysis

### Calculus

#### Algebra

#### Trigonometry

#### Limits

#### Continuity

#### Derivatives

#### Integrals

#### Multivariate Calculus

##### Partial Derivatives

##### Gradient Descent

##### Vector Fields

#### Optimization

##### Convex optimization

##### Higher order equations

##### conjugate gradient

##### Newton’s Method

### probability

### statistics

#### basic statictics

##### random variables

##### events

##### probability density functions

##### ”Introduction to Probability” by Joseph K. Blitzstein and Jessica Hwang.

#### advanced statistics

##### Markov chains

##### hidden Markov models

##### Bayesian inference

#### machine learning specific statistics

##### estimation

##### hypothesis testing

##### ”The Elements of Statistical Learning” by Trevor Hastie, Robert Tibshirani, and Jerome Friedman.

#### Regularization

##### L1 and L2 regularization

##### sparsity

## Computer Science

### Algorithms

### data structures

### software engineering

## Machine Learning

### Supervised learning

##### Linear Regression

##### Logistic Regression

##### Support Vector Machines

##### Decision Trees

##### Neural Networks

### Unsupervised learning

#### Dimensionality reduction

#### Clustering

#### Feature selection

#### Data preprocessing

#### Predictive modeling

#### Regularization

### deep learning

#### Artificial neural networks

#### Convolutional nerual networks

#### Recurrent nueral networks

#### Long short-term memory networks

#### autoencoders

### Ensembles

## Data Mining

### Data pre-processing

### feature engineering

### model evaluation

## Software Packages:

### Python

### Pandas

### TensorFlow

### scikit-learn

### keras