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gitbook_docs
  • Introduction
  • Machine Learning
    • Recommended Courses
      • For Undergrad Research
      • Math for Machine Learning
    • ML Notes
      • Covariance Correlation
      • Feature Selection
      • Linear Regression
      • Entropy, Cross-Entropy, KL Divergence
      • Bayesian Classifier
        • Terminology Review
        • Bayesian Classifier for Normally Distributed classes
      • Linear Discriminant Analysis
      • Logistic Regression
        • Logistic Regression Math
      • Logistic Regression-MaximumLikelihood
      • SVM
        • SVM concept
        • SVM math
      • Cross Validation
      • Parameter, Density Estimation
        • MAP, MLE
        • Gaussian Mixture Model
      • E-M
      • Density Estimation(non-parametric)
      • Unsupervised Learning
      • Clustering
      • kNN
      • WaveletTransform
      • Decision Tree
    • Probability and Statistics for Machine Learning
      • Introduction
      • Basics of Data Analysis
      • Probability for Discrete Random Variable
      • Poisson Distribution
      • Chi-Square Distribution
      • P-value and Statistical Hypothesis
      • Power and Sample Size
      • Hypothesis Test Old
      • Hypothesis Test
      • Multi Armed Bandit
      • Bayesian Inference
      • Bayesian Updating with Continuous Priors
      • Discrete Distribution
      • Comparison of Bayesian and frequentist inference
      • Confidence Intervals for Normal Data
      • Frequenist Methods
      • Null Hypothesis Significance Testing
      • Confidence Intervals: Three Views
      • Confidence Intervals for the Mean of Non-normal Data
      • Probabilistic Prediction
  • Industrial AI
    • PHM Dataset
    • BearingFault_Journal
      • Support Vector Machine based
      • Autoregressive(AR) model based
      • Envelope Extraction based
      • Wavelet Decomposition based
      • Prediction of RUL with Deep Convolution Nueral Network
      • Prediction of RUL with Information Entropy
      • Feature Model and Feature Selection
    • TempCore Journal
      • Machine learning of mechanical properties of steels
      • Online prediction of mechanical properties of hot rolled steel plate using machine learning
      • Prediction and Analysis of Tensile Properties of Austenitic Stainless Steel Using Artificial Neural
      • Tempcore, new process for the production of high quality reinforcing
      • TEMPCORE, the most convenient process to produce low cost high strength rebars from 8 to 75 mm
      • Experimental investigation and simulation of structure and tensile properties of Tempcore treated re
    • Notes
  • LiDAR
    • Processing of Point Cloud
    • Intro. 3D Object Detection
    • PointNet
    • PointNet++
    • Frustrum-PointNet
    • VoxelNet
    • Point RCNN
    • PointPillars
    • LaserNet
  • Simulator
    • Simulator List
    • CARLA
    • Airsim
      • Setup
      • Tutorial
        • T#1
        • T#2
        • T#3: Opencv CPP
        • T#4: Opencv Py
        • Untitled
        • T#5: End2End Driving
  • Resources
    • Useful Resources
    • Github
    • Jekyll
  • Reinforcement Learning
    • RL Overview
      • RL Bootcamp
      • MIT Deep RL
    • Textbook
    • Basics
    • Continuous Space RL
  • Unsupervised Learning
    • Introduction
  • Unclassified
    • Ethics
    • Conference Guideline
  • FPGA
    • Untitled
  • Numerical Method
    • NM API reference
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  • Statistics Roadmap
  • Linear Algebra Roadmap
  • Calculus Roadmap
  • Free YouTube resources:

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  1. Machine Learning
  2. Recommended Courses

Math for Machine Learning

PreviousFor Undergrad ResearchNextML Notes

Last updated 3 years ago

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Statistics Roadmap

1. STATS BASICS

  • Types of Data: Nominal, Ordinal, Discrete, Continuous

  • Descriptive vs Inferential Stats

  • Moments

  • Mean, Median, Mode

  • Skewness

  • Kurtosis

  • Range, IQR

  • Percentiles, Quartiles

  • Mean Deviation

  • Standard Deviation

  • Variance

  • Quartile Deviation

  • Standard Error

2. CHARTS

  • Frequency Distribution Table

  • Line Chart

  • Bar Chart

  • Histogram

  • Frequency Polygon

  • Pie Chart

  • Ogives

3. PROBABILITY DISTRIBUTION FUNCTIONS

  • Random Variables

  • Multivariate random variables

  • Discrete random variables

  • Continuous random variables

  • Law of Large Numbers

  • Expectation

  • PMF — Probability Mass Function

  • PDF — Probability Density Function

  • CDF — Cumulative Density Function

  • Bernoulli Distribution

  • Binomial Distribution

  • Geometric Distribution

  • Poisson Distribution

  • Exponential Distribution

  • Uniform Distribution

  • Gaussian / Normal Distribution

  • Chi-Square Distribution

  • Power Law Distribution

  • Pareto Distribution

  • Box-Cox Transformation

  • Log-Normal Distribution

  • Kernel Density Estimation

  • Q-Q plot

4. PROBABILITY

  • Basic Probability

  • Joint Probability

  • Conditional Probability

  • Independent Events

  • Mutually Exclusive Events

  • Bayes’ Theorem

5. TESTS / SAMPLING / POPULATION

  • Sampling, Sample Mean & Distribution

  • Central Limit Theorem

  • Point estimate, Interval estimate

  • Confidence Interval

  • Population, Population Mean & Distribution

  • Hypothesis Testing

  • P-value

  • Population Proportions

  • Critical Value

  • Significance Level

  • Rejection regions

  • Type I vs Type II errors

  • One tail vs Two tail

  • Z-Test

  • T-Test

  • ANOVA

  • F-Test

  • Chi-Square Test

  • Monte Carlo Simulation

  • A/B Testing

6. RELATIONS / REGRESSION

  • Causality

  • Covariance

  • Covariance Matrix

  • Correlation

  • Scatter Plots

  • Pearson Correlation Coefficient

  • Rank / Spearman Correlation Coefficient

  • R2 score

  • Linear Regression

  • OLS

  • Factor Analysis

  • Logistic Regression

Linear Algebra Roadmap

1. LINEAR EQUATIONS

  • Systems of Linear Equations

  • Gaussian Elimination

  • Echelon Form

  • Linear Combination

  • Span

  • Homogeneous Linear System

  • Linear Independence

  • Subspace

  • Basis

  • Affine space

  • Linear Transformation

2. MATRIX

  • Matrix transformations

  • Matrix multiplication

  • Inverse Matrix

  • Transpose of a matrix

  • Rank of a matrix

  • Symmetric Matrix

  • Orthogonal Matrix

  • Adjoint Matrix

  • Singular Matrix

  • Determinant of a matrix

  • Trace of a Matrix

3. VECTORS

  • Components of Vector

  • Vector Space

  • Norm of a vector

  • Lengths and distances

  • Euclidean Norm

  • Manhattan Norm

  • Minkowski Distance

  • Scalar Multiplication

  • Dot Product

  • Inner Product

  • Cross Product

  • Orthogonality

  • Orthonormal

  • Rotations

4. FACTORIZATION

  • Matrix Decomposition

  • LU Decomposition

  • QR Decomposition

  • Cholesky Decomposition

  • Eigen Decomposition

  • Eigen Values

  • Eigen Vector

  • Singular Value Decomposition

  • Principal Component Analysis

Calculus Roadmap

1. CALCULUS BASICS

  • Functions

  • Derivatives

  • Maxima Minima

  • Product and Chain Rule Differentiation

  • Composite functions

  • Partial Derivatives

  • Higher-order derivatives

  • Integrals

  • Limits

  • Infinite series summation

2. OPTIMIZERS

  • Gradient Descents

  • Optimizers

  • Loss Functions

  • Taylor’s Series

  • Constrained Optimization (Lagrange Multiplier)

  • Newton’s method in Optimization

  • Convex Optimization

Free YouTube resources:

Credits to 3Blue1Brown, Khan Academy, StatQuest with Josh Starmer.

Roadmap of Mathematics for Machine LearningMedium
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