AI and Machine Learning: Mathematics with Algorithmic Implementation

A book-length set of notes deriving the mathematics behind modern machine learning, from logistic regression up through deep networks, computer vision, sequence models, and probabilistic graphical models, with the algorithmic steps (forward/backward propagation, optimization) worked out by hand.

Author: Mohab M. Metwally. Started: 2020.

📄 Read the PDF

View online:https://github.com/ertosns/ai/blob/main/ml.pdf
Download (raw):https://github.com/ertosns/ai/raw/main/ml.pdf

GitHub renders the PDF directly in the browser via the View online link.

Logistic Regression as a neural network: cost function, gradient descent, forward/backward propagation, Python implementation.
Neural Networks: initialization, deep nets, optimization (momentum, RMSprop, Adam, learning-rate decay), batch normalization, multi-class classification, SVM, intro to unsupervised learning.
Structuring Machine Learning: ML strategy, orthogonality, train/dev/test sets, human-level performance, error analysis, multi-task and end-to-end learning.
Computer Vision: convolutions, Inception, object detection, bounding box prediction, neural style transfer.
Recurrent Neural Networks (RNN): forward/backward propagation through time, language models, vanishing/exploding gradients, deep RNNs, word representation, sequence-to-sequence, attention.
Probabilistic Graphical Models (PGM): Bayesian networks, reasoning patterns, d-separation, naive Bayes, template models, structured CPDs.
Natural Language Processing: pre-processing, logistic-regression and naive-Bayes classifiers, probabilistic pronunciation/spelling models.

Appendices: probabilities, covariance, singular value decomposition, exponentially weighted averages, smoothing (add-k / Laplacian), kernels and convolution functions.

Building from source

The document is a single LaTeX source, ml.tex. It uses tikz/pgfplots for all figures (no external image assets).

# with latexmk (recommended)
latexmk -pdf ml.tex
# or manually (run twice to resolve references, plus makeindex/bibtex)
pdflatex ml.tex
makeindex ml.idx
bibtex ml
pdflatex ml.tex
pdflatex ml.tex

Requires a TeX distribution (TeX Live / MiKTeX) with amsmath, hyperref, natbib, tikz, pgfplots, subcaption, chapterbib, accents, and index.

Build artifacts (.aux, .log, .toc, ...) are git-ignored; only ml.tex and the generated ml.pdf are tracked.

Status

Originally drafted in 2020 and ratified in 2026. The appendices, the empty "Summary"/"in Python" sections, and all previously-outstanding %TODO markers have been completed; the source compiles cleanly to a 139-page PDF. Remaining polish is a broader copy-edit pass and adding worked code to the CNN/RNN/PGM/NLP chapters.

License

This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). You are free to share and adapt the material for any purpose, even commercially, as long as you give appropriate credit. See the LICENSE file for the full legal text.

AI and Machine Learning: Mathematics with Algorithmic Implementation

A book-length set of notes deriving the mathematics behind modern machine learning, from logistic regression up through deep networks, computer vision, sequence models, and probabilistic graphical models, with the algorithmic steps (forward/backward propagation, optimization) worked out by hand.

Author: Mohab M. Metwally. Started: 2020.

📄 Read the PDF

View online:https://github.com/ertosns/ai/blob/main/ml.pdf
Download (raw):https://github.com/ertosns/ai/raw/main/ml.pdf

GitHub renders the PDF directly in the browser via the View online link.

Logistic Regression as a neural network: cost function, gradient descent, forward/backward propagation, Python implementation.
Neural Networks: initialization, deep nets, optimization (momentum, RMSprop, Adam, learning-rate decay), batch normalization, multi-class classification, SVM, intro to unsupervised learning.
Structuring Machine Learning: ML strategy, orthogonality, train/dev/test sets, human-level performance, error analysis, multi-task and end-to-end learning.
Computer Vision: convolutions, Inception, object detection, bounding box prediction, neural style transfer.
Recurrent Neural Networks (RNN): forward/backward propagation through time, language models, vanishing/exploding gradients, deep RNNs, word representation, sequence-to-sequence, attention.
Probabilistic Graphical Models (PGM): Bayesian networks, reasoning patterns, d-separation, naive Bayes, template models, structured CPDs.
Natural Language Processing: pre-processing, logistic-regression and naive-Bayes classifiers, probabilistic pronunciation/spelling models.

Appendices: probabilities, covariance, singular value decomposition, exponentially weighted averages, smoothing (add-k / Laplacian), kernels and convolution functions.

Building from source

The document is a single LaTeX source, ml.tex. It uses tikz/pgfplots for all figures (no external image assets).

# with latexmk (recommended)
latexmk -pdf ml.tex
# or manually (run twice to resolve references, plus makeindex/bibtex)
pdflatex ml.tex
makeindex ml.idx
bibtex ml
pdflatex ml.tex
pdflatex ml.tex

Requires a TeX distribution (TeX Live / MiKTeX) with amsmath, hyperref, natbib, tikz, pgfplots, subcaption, chapterbib, accents, and index.

Build artifacts (.aux, .log, .toc, ...) are git-ignored; only ml.tex and the generated ml.pdf are tracked.

Status

Originally drafted in 2020 and ratified in 2026. The appendices, the empty "Summary"/"in Python" sections, and all previously-outstanding %TODO markers have been completed; the source compiles cleanly to a 139-page PDF. Remaining polish is a broader copy-edit pass and adding worked code to the CNN/RNN/PGM/NLP chapters.

License

This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). You are free to share and adapt the material for any purpose, even commercially, as long as you give appropriate credit. See the LICENSE file for the full legal text.

AI and Machine Learning: Mathematics with Algorithmic Implementation

A book-length set of notes deriving the mathematics behind modern machine learning, from logistic regression up through deep networks, computer vision, sequence models, and probabilistic graphical models, with the algorithmic steps (forward/backward propagation, optimization) worked out by hand.

Author: Mohab M. Metwally. Started: 2020.

📄 Read the PDF

View online:https://github.com/ertosns/ai/blob/main/ml.pdf
Download (raw):https://github.com/ertosns/ai/raw/main/ml.pdf

GitHub renders the PDF directly in the browser via the View online link.

Logistic Regression as a neural network: cost function, gradient descent, forward/backward propagation, Python implementation.
Neural Networks: initialization, deep nets, optimization (momentum, RMSprop, Adam, learning-rate decay), batch normalization, multi-class classification, SVM, intro to unsupervised learning.
Structuring Machine Learning: ML strategy, orthogonality, train/dev/test sets, human-level performance, error analysis, multi-task and end-to-end learning.
Computer Vision: convolutions, Inception, object detection, bounding box prediction, neural style transfer.
Recurrent Neural Networks (RNN): forward/backward propagation through time, language models, vanishing/exploding gradients, deep RNNs, word representation, sequence-to-sequence, attention.
Probabilistic Graphical Models (PGM): Bayesian networks, reasoning patterns, d-separation, naive Bayes, template models, structured CPDs.
Natural Language Processing: pre-processing, logistic-regression and naive-Bayes classifiers, probabilistic pronunciation/spelling models.

Appendices: probabilities, covariance, singular value decomposition, exponentially weighted averages, smoothing (add-k / Laplacian), kernels and convolution functions.

Building from source

The document is a single LaTeX source, ml.tex. It uses tikz/pgfplots for all figures (no external image assets).

# with latexmk (recommended)
latexmk -pdf ml.tex
# or manually (run twice to resolve references, plus makeindex/bibtex)
pdflatex ml.tex
makeindex ml.idx
bibtex ml
pdflatex ml.tex
pdflatex ml.tex

Requires a TeX distribution (TeX Live / MiKTeX) with amsmath, hyperref, natbib, tikz, pgfplots, subcaption, chapterbib, accents, and index.

Build artifacts (.aux, .log, .toc, ...) are git-ignored; only ml.tex and the generated ml.pdf are tracked.

Status

Originally drafted in 2020 and ratified in 2026. The appendices, the empty "Summary"/"in Python" sections, and all previously-outstanding %TODO markers have been completed; the source compiles cleanly to a 139-page PDF. Remaining polish is a broader copy-edit pass and adding worked code to the CNN/RNN/PGM/NLP chapters.

License

This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). You are free to share and adapt the material for any purpose, even commercially, as long as you give appropriate credit. See the LICENSE file for the full legal text.

AI and Machine Learning: Mathematics with Algorithmic Implementation

A book-length set of notes deriving the mathematics behind modern machine learning, from logistic regression up through deep networks, computer vision, sequence models, and probabilistic graphical models, with the algorithmic steps (forward/backward propagation, optimization) worked out by hand.

Author: Mohab M. Metwally. Started: 2020.

📄 Read the PDF

View online:https://github.com/ertosns/ai/blob/main/ml.pdf
Download (raw):https://github.com/ertosns/ai/raw/main/ml.pdf

GitHub renders the PDF directly in the browser via the View online link.

Logistic Regression as a neural network: cost function, gradient descent, forward/backward propagation, Python implementation.
Neural Networks: initialization, deep nets, optimization (momentum, RMSprop, Adam, learning-rate decay), batch normalization, multi-class classification, SVM, intro to unsupervised learning.
Structuring Machine Learning: ML strategy, orthogonality, train/dev/test sets, human-level performance, error analysis, multi-task and end-to-end learning.
Computer Vision: convolutions, Inception, object detection, bounding box prediction, neural style transfer.
Recurrent Neural Networks (RNN): forward/backward propagation through time, language models, vanishing/exploding gradients, deep RNNs, word representation, sequence-to-sequence, attention.
Probabilistic Graphical Models (PGM): Bayesian networks, reasoning patterns, d-separation, naive Bayes, template models, structured CPDs.
Natural Language Processing: pre-processing, logistic-regression and naive-Bayes classifiers, probabilistic pronunciation/spelling models.

Appendices: probabilities, covariance, singular value decomposition, exponentially weighted averages, smoothing (add-k / Laplacian), kernels and convolution functions.

Building from source

The document is a single LaTeX source, ml.tex. It uses tikz/pgfplots for all figures (no external image assets).

# with latexmk (recommended)
latexmk -pdf ml.tex
# or manually (run twice to resolve references, plus makeindex/bibtex)
pdflatex ml.tex
makeindex ml.idx
bibtex ml
pdflatex ml.tex
pdflatex ml.tex

Requires a TeX distribution (TeX Live / MiKTeX) with amsmath, hyperref, natbib, tikz, pgfplots, subcaption, chapterbib, accents, and index.

Build artifacts (.aux, .log, .toc, ...) are git-ignored; only ml.tex and the generated ml.pdf are tracked.

Status

Originally drafted in 2020 and ratified in 2026. The appendices, the empty "Summary"/"in Python" sections, and all previously-outstanding %TODO markers have been completed; the source compiles cleanly to a 139-page PDF. Remaining polish is a broader copy-edit pass and adding worked code to the CNN/RNN/PGM/NLP chapters.

License

This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). You are free to share and adapt the material for any purpose, even commercially, as long as you give appropriate credit. See the LICENSE file for the full legal text.

AI and Machine Learning: Mathematics with Algorithmic Implementation

A book-length set of notes deriving the mathematics behind modern machine learning, from logistic regression up through deep networks, computer vision, sequence models, and probabilistic graphical models, with the algorithmic steps (forward/backward propagation, optimization) worked out by hand.

Author: Mohab M. Metwally. Started: 2020.

📄 Read the PDF

View online:https://github.com/ertosns/ai/blob/main/ml.pdf
Download (raw):https://github.com/ertosns/ai/raw/main/ml.pdf

GitHub renders the PDF directly in the browser via the View online link.

Logistic Regression as a neural network: cost function, gradient descent, forward/backward propagation, Python implementation.
Neural Networks: initialization, deep nets, optimization (momentum, RMSprop, Adam, learning-rate decay), batch normalization, multi-class classification, SVM, intro to unsupervised learning.
Structuring Machine Learning: ML strategy, orthogonality, train/dev/test sets, human-level performance, error analysis, multi-task and end-to-end learning.
Computer Vision: convolutions, Inception, object detection, bounding box prediction, neural style transfer.
Recurrent Neural Networks (RNN): forward/backward propagation through time, language models, vanishing/exploding gradients, deep RNNs, word representation, sequence-to-sequence, attention.
Probabilistic Graphical Models (PGM): Bayesian networks, reasoning patterns, d-separation, naive Bayes, template models, structured CPDs.
Natural Language Processing: pre-processing, logistic-regression and naive-Bayes classifiers, probabilistic pronunciation/spelling models.

Appendices: probabilities, covariance, singular value decomposition, exponentially weighted averages, smoothing (add-k / Laplacian), kernels and convolution functions.

Building from source

The document is a single LaTeX source, ml.tex. It uses tikz/pgfplots for all figures (no external image assets).

# with latexmk (recommended)
latexmk -pdf ml.tex
# or manually (run twice to resolve references, plus makeindex/bibtex)
pdflatex ml.tex
makeindex ml.idx
bibtex ml
pdflatex ml.tex
pdflatex ml.tex

Requires a TeX distribution (TeX Live / MiKTeX) with amsmath, hyperref, natbib, tikz, pgfplots, subcaption, chapterbib, accents, and index.

Build artifacts (.aux, .log, .toc, ...) are git-ignored; only ml.tex and the generated ml.pdf are tracked.

Status

Originally drafted in 2020 and ratified in 2026. The appendices, the empty "Summary"/"in Python" sections, and all previously-outstanding %TODO markers have been completed; the source compiles cleanly to a 139-page PDF. Remaining polish is a broader copy-edit pass and adding worked code to the CNN/RNN/PGM/NLP chapters.

License

This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). You are free to share and adapt the material for any purpose, even commercially, as long as you give appropriate credit. See the LICENSE file for the full legal text.

AI and Machine Learning: Mathematics with Algorithmic Implementation

A book-length set of notes deriving the mathematics behind modern machine learning, from logistic regression up through deep networks, computer vision, sequence models, and probabilistic graphical models, with the algorithmic steps (forward/backward propagation, optimization) worked out by hand.

Author: Mohab M. Metwally. Started: 2020.

📄 Read the PDF

View online:https://github.com/ertosns/ai/blob/main/ml.pdf
Download (raw):https://github.com/ertosns/ai/raw/main/ml.pdf

GitHub renders the PDF directly in the browser via the View online link.

Logistic Regression as a neural network: cost function, gradient descent, forward/backward propagation, Python implementation.
Neural Networks: initialization, deep nets, optimization (momentum, RMSprop, Adam, learning-rate decay), batch normalization, multi-class classification, SVM, intro to unsupervised learning.
Structuring Machine Learning: ML strategy, orthogonality, train/dev/test sets, human-level performance, error analysis, multi-task and end-to-end learning.
Computer Vision: convolutions, Inception, object detection, bounding box prediction, neural style transfer.
Recurrent Neural Networks (RNN): forward/backward propagation through time, language models, vanishing/exploding gradients, deep RNNs, word representation, sequence-to-sequence, attention.
Probabilistic Graphical Models (PGM): Bayesian networks, reasoning patterns, d-separation, naive Bayes, template models, structured CPDs.
Natural Language Processing: pre-processing, logistic-regression and naive-Bayes classifiers, probabilistic pronunciation/spelling models.

Appendices: probabilities, covariance, singular value decomposition, exponentially weighted averages, smoothing (add-k / Laplacian), kernels and convolution functions.

Building from source

The document is a single LaTeX source, ml.tex. It uses tikz/pgfplots for all figures (no external image assets).

# with latexmk (recommended)
latexmk -pdf ml.tex
# or manually (run twice to resolve references, plus makeindex/bibtex)
pdflatex ml.tex
makeindex ml.idx
bibtex ml
pdflatex ml.tex
pdflatex ml.tex

Requires a TeX distribution (TeX Live / MiKTeX) with amsmath, hyperref, natbib, tikz, pgfplots, subcaption, chapterbib, accents, and index.

Build artifacts (.aux, .log, .toc, ...) are git-ignored; only ml.tex and the generated ml.pdf are tracked.

Status

Originally drafted in 2020 and ratified in 2026. The appendices, the empty "Summary"/"in Python" sections, and all previously-outstanding %TODO markers have been completed; the source compiles cleanly to a 139-page PDF. Remaining polish is a broader copy-edit pass and adding worked code to the CNN/RNN/PGM/NLP chapters.

License

This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). You are free to share and adapt the material for any purpose, even commercially, as long as you give appropriate credit. See the LICENSE file for the full legal text.

AI and Machine Learning: Mathematics with Algorithmic Implementation

A book-length set of notes deriving the mathematics behind modern machine learning, from logistic regression up through deep networks, computer vision, sequence models, and probabilistic graphical models, with the algorithmic steps (forward/backward propagation, optimization) worked out by hand.

Author: Mohab M. Metwally. Started: 2020.

📄 Read the PDF

View online:https://github.com/ertosns/ai/blob/main/ml.pdf
Download (raw):https://github.com/ertosns/ai/raw/main/ml.pdf

GitHub renders the PDF directly in the browser via the View online link.

Logistic Regression as a neural network: cost function, gradient descent, forward/backward propagation, Python implementation.
Neural Networks: initialization, deep nets, optimization (momentum, RMSprop, Adam, learning-rate decay), batch normalization, multi-class classification, SVM, intro to unsupervised learning.
Structuring Machine Learning: ML strategy, orthogonality, train/dev/test sets, human-level performance, error analysis, multi-task and end-to-end learning.
Computer Vision: convolutions, Inception, object detection, bounding box prediction, neural style transfer.
Recurrent Neural Networks (RNN): forward/backward propagation through time, language models, vanishing/exploding gradients, deep RNNs, word representation, sequence-to-sequence, attention.
Probabilistic Graphical Models (PGM): Bayesian networks, reasoning patterns, d-separation, naive Bayes, template models, structured CPDs.
Natural Language Processing: pre-processing, logistic-regression and naive-Bayes classifiers, probabilistic pronunciation/spelling models.

Appendices: probabilities, covariance, singular value decomposition, exponentially weighted averages, smoothing (add-k / Laplacian), kernels and convolution functions.

Building from source

The document is a single LaTeX source, ml.tex. It uses tikz/pgfplots for all figures (no external image assets).

# with latexmk (recommended)
latexmk -pdf ml.tex
# or manually (run twice to resolve references, plus makeindex/bibtex)
pdflatex ml.tex
makeindex ml.idx
bibtex ml
pdflatex ml.tex
pdflatex ml.tex

Requires a TeX distribution (TeX Live / MiKTeX) with amsmath, hyperref, natbib, tikz, pgfplots, subcaption, chapterbib, accents, and index.

Build artifacts (.aux, .log, .toc, ...) are git-ignored; only ml.tex and the generated ml.pdf are tracked.

Status

Originally drafted in 2020 and ratified in 2026. The appendices, the empty "Summary"/"in Python" sections, and all previously-outstanding %TODO markers have been completed; the source compiles cleanly to a 139-page PDF. Remaining polish is a broader copy-edit pass and adding worked code to the CNN/RNN/PGM/NLP chapters.

License

This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). You are free to share and adapt the material for any purpose, even commercially, as long as you give appropriate credit. See the LICENSE file for the full legal text.

AI and Machine Learning: Mathematics with Algorithmic Implementation

A book-length set of notes deriving the mathematics behind modern machine learning, from logistic regression up through deep networks, computer vision, sequence models, and probabilistic graphical models, with the algorithmic steps (forward/backward propagation, optimization) worked out by hand.

Author: Mohab M. Metwally. Started: 2020.

📄 Read the PDF

View online:https://github.com/ertosns/ai/blob/main/ml.pdf
Download (raw):https://github.com/ertosns/ai/raw/main/ml.pdf

GitHub renders the PDF directly in the browser via the View online link.

Logistic Regression as a neural network: cost function, gradient descent, forward/backward propagation, Python implementation.
Neural Networks: initialization, deep nets, optimization (momentum, RMSprop, Adam, learning-rate decay), batch normalization, multi-class classification, SVM, intro to unsupervised learning.
Structuring Machine Learning: ML strategy, orthogonality, train/dev/test sets, human-level performance, error analysis, multi-task and end-to-end learning.
Computer Vision: convolutions, Inception, object detection, bounding box prediction, neural style transfer.
Recurrent Neural Networks (RNN): forward/backward propagation through time, language models, vanishing/exploding gradients, deep RNNs, word representation, sequence-to-sequence, attention.
Probabilistic Graphical Models (PGM): Bayesian networks, reasoning patterns, d-separation, naive Bayes, template models, structured CPDs.
Natural Language Processing: pre-processing, logistic-regression and naive-Bayes classifiers, probabilistic pronunciation/spelling models.

Appendices: probabilities, covariance, singular value decomposition, exponentially weighted averages, smoothing (add-k / Laplacian), kernels and convolution functions.

Building from source

The document is a single LaTeX source, ml.tex. It uses tikz/pgfplots for all figures (no external image assets).

# with latexmk (recommended)
latexmk -pdf ml.tex
# or manually (run twice to resolve references, plus makeindex/bibtex)
pdflatex ml.tex
makeindex ml.idx
bibtex ml
pdflatex ml.tex
pdflatex ml.tex

Requires a TeX distribution (TeX Live / MiKTeX) with amsmath, hyperref, natbib, tikz, pgfplots, subcaption, chapterbib, accents, and index.

Build artifacts (.aux, .log, .toc, ...) are git-ignored; only ml.tex and the generated ml.pdf are tracked.

Status

Originally drafted in 2020 and ratified in 2026. The appendices, the empty "Summary"/"in Python" sections, and all previously-outstanding %TODO markers have been completed; the source compiles cleanly to a 139-page PDF. Remaining polish is a broader copy-edit pass and adding worked code to the CNN/RNN/PGM/NLP chapters.

License

This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). You are free to share and adapt the material for any purpose, even commercially, as long as you give appropriate credit. See the LICENSE file for the full legal text.

Name		Name	Last commit message	Last commit date
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
bib0.bib		bib0.bib
ml.pdf		ml.pdf
ml.tex		ml.tex

Name		Name	Last commit message	Last commit date
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
bib0.bib		bib0.bib
ml.pdf		ml.pdf
ml.tex		ml.tex

Name		Name	Last commit message	Last commit date
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
bib0.bib		bib0.bib
ml.pdf		ml.pdf
ml.tex		ml.tex

Name		Name	Last commit message	Last commit date
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
bib0.bib		bib0.bib
ml.pdf		ml.pdf
ml.tex		ml.tex

Name		Name	Last commit message	Last commit date
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
bib0.bib		bib0.bib
ml.pdf		ml.pdf
ml.tex		ml.tex

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AI and Machine Learning: Mathematics with Algorithmic Implementation

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AI and Machine Learning: Mathematics with Algorithmic Implementation

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AI and Machine Learning: Mathematics with Algorithmic Implementation

📄 Read the PDF

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AI and Machine Learning: Mathematics with Algorithmic Implementation

📄 Read the PDF

Contents

Building from source

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Contributors

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