Reframes "can machines think?" as a concrete test: the imitation game, now the Turing test, where a machine passes if its typed replies are indistinguishable from a human's. Rebuts nine objections and backs machines that learn like children.
Models the brain probabilistically and proposes the perceptron: weighted threshold units that learn to classify patterns by adjusting connection strengths from examples, rather than storing fixed memories. The 1958 root of trainable neural networks.
Introduces the generalized delta rule — backpropagation — for training multi-layer networks with hidden units by gradient descent on output error, letting hidden layers learn internal representations that solve problems single-layer networks cannot.
Treats a network's weights as a noisy channel and penalizes the bits needed to describe them, formalizing the "bits-back" coding trick — an early variational argument later recognized as a conceptual ancestor of the VAE.
Reframes model selection as data compression: the best hypothesis is the one that lets you describe the data in the fewest bits. Walks through MDL twice — once conceptually, once with full math — turning Occam's razor into a usable inference principle.
A graduate text teaching machine learning through a unified Bayesian lens, treating classification, regression, and clustering as inference over distributions. Covers graphical models, EM, kernels, and approximate inference with derivations.
Frames machine learning through the lens of statistics, treating each method as an estimator with bias, variance, and inferential meaning, not a black box. Covers linear models through boosting, SVMs, and graphical models, math made explicit.
Gives intelligence a falsifiable mathematical definition — an agent's expected reward across all computable environments, weighted by simplicity — turning a fuzzy word into the Universal Intelligence Measure built on AIXI and Solomonoff induction.
Argues that "interesting" complexity is low in both ordered and fully random states but peaks in between, and proposes "complextropy" — a resource-bounded Kolmogorov-complexity measure — to capture the rise-then-fall pattern entropy can't explain.
Graduate-level ML textbook that frames nearly every method as Bayesian inference under one probabilistic lens, from linear models to deep nets and graphical models. Encyclopedic at ~1100 pages, math-heavy, with MATLAB code.
The result that kicked off the deep learning era: in 2012 a deep CNN cut ImageNet top-5 error from 26% to 15%, showing that GPU-trained networks with ReLU and dropout could beat decades of hand-engineered computer vision features.
Measures why complexity in closed systems rises then falls while entropy only climbs, using a coffee-and-cream cellular automaton. The key result: only interacting particles produce a transient complexity peak; non-interacting ones never do.