Index
Abagnale, Frank, Jr., 177, 306
aboutthedata.com, 272
A/B testing, 227, 309
Accuracy, 75-79, 87, 241, 243
Ackley, David, 103
Action potentials, 95-96, 104-105
Acxiom, 272
Adam, the robot scientist, 16, 84, 299
Adaptive systems, 8. See also Machine learning
AdSense system, 160
AI. See Artificial intelligence (AI)
AIDS vaccine, Bayesian networks and, 159-160
Alchemy, 246-259, 309
Markov logic networks and, 246-250
shortcomings, 255-259
tribes of machine learning and, 250-255
alchemy.cs.washington.edu, 250
Algorithms
classifiers, 86-87
complexity monster and, 5-6
defined, 1
designing, 4-5
further readings, 298-299
genetic, 122-128
overview, 1-6
structure mapping, 199-200
See also Machine learning; individual algorithms
AlphaDog, 21
Amazon, 198, 266, 291
A/B testing and, 227
data gathering, 211, 271, 272
machine learning and, 11, 12
Mechanical Turk, 14
recommendations, 12-13, 42, 184, 268, 286
Analogical reasoning, 179, 197
Analogizers, 51, 53, 54, 172-173
Alchemy and, 253-254
case-based reasoning, 197-200
dimensionality, 186-190
Master Algorithm and, 240-241
nearest-neighbor algorithm, 178-186
similiarity and, 179
support vector machines, 53, 190-196
symbolists vs., 200-202
Analogy, 175-179, 197-200
AND gate, 96
AND operation, 2
Anna Karenina (Tolstoy), 66
Apple, 272
Aristotle, 58, 64, 72, 178, 243
Artificial intelligence (AI)
human control of, 282-284
knowledge engineers and, 35-36
machine learning and, 8, 89-90
ASIC (application-specific integrated circuit) design, 49
Asimov, Isaac, 232, 280
Assumptions
ill-posed problem and, 64
of learners, 44
learning from finite data and, 24-25
prior, 174
simplifying to reduce number of probabilities, 150
symbolists and, 61-62
Atlantic (magazine), 273-274
AT &T, 272
Attribute selection, 186-187, 188-189
Attribute weights, 189
Auditory cortex, 26
Autoencoder, 116-118
Automation, machine learning and, 10
Automaton, 123
The Average American (O’Keefe), 206
Average member, 206
Axon, 95
Babbage, Charles, 28
Backpropagation (backprop), 52, 104, 107-111, 115, 302
Alchemy and, 252
genetic algorithms vs., 128
neural networks and, 112-114
reinforcement learning and, 222
Bagging, 238
Baldwin, J. M., 138-139
Baldwin effect, 139, 140, 304
Bandit problems, 129-130
Barto, Andy, 221
Bayes, Thomas, 144-145
Bayesian learning, 166-170, 174-175
Bayesian methods, cell model and, 114
Bayesian model averaging, 166-167
Bayesian models, tweaking probabilities, 170-173
Bayesian networks, 24, 156-161, 305-306
Alchemy and, 250
gene regulation and, 159
inference problem and, 161-166
Master Algorithm and, 240, 245
relational learning and, 231
Bayesians, 51, 52-53, 54, 143-175
Alchemy and, 253
further reading, 304-305
hidden Markov model, 154-155
If… then… rules and, 155-156
inference problem, 161-166
learning and, 166-170
logic and probability and, 173-175
Markov chain, 153-155
Markov networks, 170-173
Master Algorithm and, 240-241, 242
medical diagnosis and, 149-150
models and, 149-153
nature and, 141
probabilistic inference and, 52, 53
See also Bayesian networks
Bayes’ theorem, 31-32, 52-53, 143-149, 253
Beam search, 135
“Beer and diapers” rule, 69-70
Belief, probability and, 149
Belief propagation, 161-164, 242, 253
Bell Labs, 190
Bellman, Richard, 188, 220
Bellman’s equation, 220
Berkeley, George, 58
Berlin, Isaiah, 41
Bias, 78-79
Bias-free learning, futility of, 64
Bias-variance decomposition, 301
The Bible Code (Drosnin), 72
Big data, 21
A/B testing and, 227
algorithms and, 7
clustering and, 206-207
relational learning and, 232-233
science, machine learning, and, 14-16
scientific truth and, 40
Big-data systems, 258
Bing, 12
Biology, learning algorithms and, 15
Black swans, 38-39, 158, 232
The Black Swan (Taleb), 38
Blessing of nonuniformity, 189
Board games, reinforcement learning and, 219
Bohr, Niels, 178, 199
Boltzmann distribution, 103-104
Boltzmann machines, 103-104, 117, 250
Boole, George, 104, 175
Boolean circuits, 123, 136
Boolean variable, 149
Boosting, 238
Borges, Jorge Luis, 71
Box, George, 151
Brahe, Tycho, 14, 131
Brahe phase of science, 39-40
Brain
learning algorithms and, 26-28
mapping, 118
number of connections in, 94-95
reverse engineering the, 52, 302
S curves and, 105
simulating with computer, 95
spin glasses and, 102-103
BRAIN initiative, 118
Breiman, Leo, 238
Brin, Sergey, 55, 227, 274
Bryson, Arthur, 113
Bucket brigade algorithm, 127
Building blocks, 128-129, 134
Buntine, Wray, 80
Burglar alarms, Bayesian networks and, 157-158
Burks, Arthur, 123
Burns, Bob, 206
Business, machine learning and, 10-13
C. elegans, 118
Cajal, Santiago Ramón y, 93-94
Caltech, 170
CancerCommons.org, 261
Cancer cure
algorithm for, 53-54
Bayesian learning and, 174
inverse deduction and, 83-85
Markov logic network and, 249
program for (CanceRx), 259-261, 310
Cancer diagnosis, 141
Cancer drugs
predicting efficacy of, 83-84
relational learning and models for, 233
selection of, 41-42
CanceRx, 259-261, 310
Capital One, 272
Carbonell, Jaime, 69
Carnap, Rudolf, 175
Cars
driverless, 113, 166, 172, 306
learning to drive, 113
Case-based reasoning, 198, 307
Catch Me If You Can (film), 177
Cause and effect, Bayes’ theorem and, 145-149
Cell
model of, 114-115
relational learning and workings of, 233
Cell assembly, 94
Cell phone, hidden Markov models and, 155
Centaurs, 277
Central Dogma, 83
Cerebellum, 27, 118
Chance, Bayes and, 145
Chaos, study of, 30
Checkers-playing program, 219
Cholera outbreak, London’s, 182-183
Chomsky, Noam, 36-38
Chrome, 266
Chunking, 223-227, 254, 309
Circuit design, genetic programming and, 135-136
Classes, 86-87, 209, 257
Classifiers, 86-87, 127
Master Algorithm and, 240
Naïve Bayes, 151-153
nearest-neighbor algorithm and, 183
Clinton, Bill, 18
Clustering, 205-210, 254, 257
hierarchical, 210