Machine Learning Mathematics Roadmap — How much math is required?

Machine Learning by Himanshu Ramchandani

Linear Algebra, Statistics, Probability, Objective Functions,
Regularization, Information Theory, Optimization, Distribution

Contents

FREE Resources -

Chapter 1 — Linear Algebra

Chapter 2 — Statistics

Chapter 3 — Probability

Chapter 4 — Objective Functions

Chapter 5 — Regularization

Chapter 6 — Information Theory

Chapter 7 — Optimization

Chapter 8 — Distribution

This phase is different from books that are available on the internet. I included all the topics required to understand the whole architecture of Machine Learning algorithms.

Examples of how and where these mathematical equations are used and Interview Questions can be asked of an ML Engineer while hiring.

We will learn different concepts individually, converting mathematical equations into Python programming expressions along with their examples in the real world.

math meme by Himanshu Ramchandani

FREE Resources →

Mathematics for Machine Learning

Algebra, Topology, Differential Calculus, and Optimization Theory For Computer Science and Machine Learning

All math topics for Machine Learning by Stanford

Stanford CS229: Machine Learning Course | Summer 2019 (Anand Avati)

Chapter 1 — Linear Algebra

Learn for FREE — Mathematics for ML — Linear Algebra

Mathematics for Machine Learning — Linear Algebra

1 | Vectors

2 | Matrix

3 | Eigenvalues and Eigenvectors

3 | Factorization

4 | Singular Value Decomposition (SVD)

5 | Gradient

6 | Tensors

7 | Jacobian Matrix

8 | Curse of Dimensionality

Chapter 2 — Statistics

The Element of Statistical Learning

Elements of Statistical Learning: data mining, inference, and prediction. 2nd Edition.

Statistics give us 2 tools descriptive and inferential

1 | Descriptive Statistics

1 | Variables

2 | Mean

3 | Median

4 | Mode

5 | Standard Deviation

6 | Variance

7 | Range

8 | Percentile

9 | Skewness

10 | Kurtosis

2 | Inferential Statistics

1 | Sampling Distributions

2 | Central Limit Theorem

3 | Hypothesis Testing

4 | Confidence Intervals

5 | T-Tests

6 | Analysis of Variance (ANOVA)

7 | Chi-Square Test

8 | Regression Analysis

9 | Bayesian Inference

10 | Maximum Likelihood Estimation (MLE)

Chapter 3 — Probability

Probability Theory: The Logic of Science

https://bayes.wustl.edu/etj/prob/book.pdf

1 | Probability Distribution

2 | Conditional Probability

3 | Bayes’ Theorem

4 | Joint and Marginal Probabilities

5 | Independence and Conditional Independence

Chapter 4 — Objective Functions

1 | Mean Squared Error (MSE)

2 | Mean Absolute Error (MAE)

3 | Huber Loss

4 | Binary Cross-Entropy (Log Loss)

5 | Categorical Cross-Entropy

6 | Maximum Likelihood Estimation (MLE)

7 | Sparse Categorical Cross-Entropy

8 | Hinge Loss

9 | Kullback-Leibler Divergence

10 | Gini Impurity

11 | Others

Chapter 5 — Regularization

1 | L1 Regularization (Lasso Regression)

2 | L2 Regularization (Ridge Regression)

3 | Elastic Net Regularization

4 | Dropout Regularization

5 | Data Augmentation

6 | Early Stopping

7 | Max-Norm Regularization

8 | Batch Normalization

9 | Weight Decay

10 | Total Variation Regularization

Chapter 6 — Information Theory

Information Theory, Inference and Learning Algorithms

David MacKay: Information Theory, Pattern Recognition and Neural Networks: The Book

1 | Entropy

2 | Conditional Entropy

3 | Joint Entropy

4 | Mutual Information

5 | Relative Entropy (Kullback-Leibler Divergence)

6 | Cross-Entropy

7 | Information Gain

8 | Shannon-Fano Coding

9 | Huffman Coding

10 | Data Entropy

Chapter 7 — Optimization

1 | Gradient Descent

2 | Stochastic Gradient Descent (SGD)

3 | Mini-Batch Gradient Descent

4 | Momentum

5 | Nesterov Accelerated Gradient (NAG)

6 | Adagrad (Adaptive Gradient Algorithm)

7 | RMSprop (Root Mean Square Propagation)

8 | Adam (Adaptive Moment Estimation)

Chapter 8 — Distribution

1 | Bernoulli Distribution

2 | Binomial Distribution

3 | Multinomial Distribution

4 | Normal (Gaussian) Distribution

5 | Uniform Distribution

6 | Exponential Distribution

7 | Poisson Distribution

Calculus

Calculus 1 | Math | Khan Academy

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Session Structure →

1. Topic Understanding
2. Real-world examples
3. Implementation in Python
4. Interview questions on that topic
5. Questions to implement in programming
6. Reading one blog or reacting on the resource in the machine learning space
7. Study one company hiring in Machine Learning at a time, and analyze their products and services.
8. Discussion on How to be better?

About me (Your Mentor)

I am Himanshu Ramchandani a Data & Engineering Consultant. I help enterprises utilize big data to build AI-powered products & Mentor professionals to improve their skills in the data field by 1% every day.

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