7. Foundations of Data Science by Avrim Blum, John Hopcroft, and Ravindran Kannan
One of the surprises of computer science is that some domain-independent methods have been immensely successful in tackling problems from diverse areas. Machine learning is a striking example.
Chapter 5 describes the foundations of machine learning, both algorithms for optimizing over given training examples, as
well as the theory for understanding when such optimization can be expected to lead to good performance on new, unseen data. This includes important measures such as the Vapnik-Chervonenkis dimension, important algorithms such as the Perceptron Algorithm,
stochastic gradient descent, boosting, and deep learning, and important notions such as
regularization and overfitting.
8. Gaussian Processes for Machine Learning by Carl Edward Rasmussen and Christopher K. I. Williams
This book is concerned with supervised learning, which is the problem of learning input-output mappings from empirical data (the training dataset).
Depending on the characteristics of the output, this problem is known as either regression, for continuous outputs, or classification, when outputs are discrete.
The book is a comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines.
9. Machine Learning Engineering by Andriy Burkov
This book is based on the author’s own 15 years of experience in solving problems with AI as well as on the published experience of the industry leaders.
It is filled with best practices and design patterns of building reliable machine learning solutions that scale.
The author has a Ph.D. in AI and is the leader of a machine learning team at Gartner.
10. Hands-On Machine Learning with R by Bradley Boehmke and Brandon Greenwell
This book provides hands-on modules for many of the most common machine learning methods to include:
- Generalized low rank models
- Clustering algorithms
- Autoencoders
- Regularized models
- Random forests
- Gradient boosting machines
- Deep neural networks
- Stacking / super learners
- and more!
11. Information Theory, Inference, and Learning Algorithms by David MacKay
Conventional courses on information theory cover not only the beautiful theoretical ideas of Shannon, but also practical solutions to communication problems. This book goes further, bringing in Bayesian data modelling, Monte Carlo methods, variational methods, clustering algorithms, and neural networks.
This book is aimed at senior undergraduates and graduate students in Engineering, Science, Mathematics, and Computing.
12. Algebra, Topology, Differential Calculus, and Optimization Theory For Computer Science and Machine Learning
This is a huge book spanning 2,200 pages. It’s divided into the following topics:
- Groups, Rings, and Fields.
- Linear Algebra:
- Vector Spaces, Bases, Linear Maps
- Matrices and Linear Maps
- Haar Bases, Haar Wavelets, Hadamard Matrices
- Direct Sums
- Determinants
- Gaussian Elimination, LU, Cholesky, Echelon Form
- Vector Norms and Matrix Norms
- Iterative Methods for Solving Linear Systems
- The Dual Space and Duality
- Euclidean Spaces
- QR-Decomposition for Arbitrary Matrices
- Hermitian Spaces
- Eigenvectors and Eigenvalues
- Unit Quaternions and Rotations in SO(3)
- Spectral Theorems
- Computing Eigenvalues and Eigenvectors
- Introduction to The Finite Elements Method
- Graphs and Graph Laplacians; Basic Facts
- Spectral Graph Drawing
- Singular Value Decomposition and Polar Form
- Applications of SVD and Pseudo-Inverses
- Basics of Affine Geometry
- Affine and Projective Geometry:
- Embedding an Affine Space in a Vector Space
- The Cartan–Dieudonné Theorem
- The Geometry of Bilinear Forms; Witt’s Theorem.
- Algebra: PID’s, UFD’s, Noetherian Rings, Tensors,
Modules over a PID, Normal Forms - Topology, Differential Calculus
and more.
Pages in this article:
Page 1: Understanding Machine Learning and more books
Page 2: Foundations of Data Science and more books
Page 3: Automated Machine Learning and more books