{"product_id":"machine-learning-and-big-data-with-kdbq-9781119404750","title":"Machine Learning and Big Data with kdbq","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eUpgrade your programming language to more effectively handle high-frequency data  Machine Learning and Big Data with KDB+\/Q offers quants, programmers and algorithmic traders a practical entry into the powerful but non-intuitive kdb+ database and q programming language. Ideally designed to handle the speed and volume of high-frequency financial data at sell- and buy-side institutions, these tools have become the de facto standard; this book provides the foundational knowledge practitioners need to work effectively with this rapidly-evolving approach to analytical trading.    The discussion follows the natural progression of working strategy development to allow hands-on learning in a familiar sphere, illustrating the contrast of efficiency and capability between the q language and other programming approaches. Rather than an all-encompassing bible-type reference, this book is designed with a focus on real-world practicality to help you quickly get up to speed and become productive with\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface xvii\u003c\/p\u003e \u003cp\u003eAbout the Authors xxiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart One Language Fundamentals\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Fundamentals of the q Programming Language 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 The (Not So Very) First Steps in q 3\u003c\/p\u003e \u003cp\u003e1.2 Atoms and Lists 5\u003c\/p\u003e \u003cp\u003e1.2.1 Casting Types 11\u003c\/p\u003e \u003cp\u003e1.3 Basic Language Constructs 14\u003c\/p\u003e \u003cp\u003e1.3.1 Assigning, Equality and Matching 14\u003c\/p\u003e \u003cp\u003e1.3.2 Arithmetic Operations and Right-to-Left Evaluation: Introduction to q Philosophy 17\u003c\/p\u003e \u003cp\u003e1.4 Basic Operators 19\u003c\/p\u003e \u003cp\u003e1.5 Difference between Strings and Symbols 31\u003c\/p\u003e \u003cp\u003e1.5.1 Enumeration 31\u003c\/p\u003e \u003cp\u003e1.6 Matrices and Basic Linear Algebra in q 33\u003c\/p\u003e \u003cp\u003e1.7 Launching the Session: Additional Options 35\u003c\/p\u003e \u003cp\u003e1.8 Summary and How-To’s 38\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Dictionaries and Tables: The q Fundamentals 41\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Dictionary 41\u003c\/p\u003e \u003cp\u003e2.2 Table 44\u003c\/p\u003e \u003cp\u003e2.3 The Truth about Tables 48\u003c\/p\u003e \u003cp\u003e2.4 Keyed Tables are Dictionaries 50\u003c\/p\u003e \u003cp\u003e2.5 From a Vector Language to an Algebraic Language 51\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Functions 57\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Namespace 59\u003c\/p\u003e \u003cp\u003e3.1.0.1 .quantQ. Namespace 60\u003c\/p\u003e \u003cp\u003e3.2 The Six Adverbs 60\u003c\/p\u003e \u003cp\u003e3.2.1 Each 60\u003c\/p\u003e \u003cp\u003e3.2.1.1 Each 61\u003c\/p\u003e \u003cp\u003e3.2.1.2 Each-left \\: 61\u003c\/p\u003e \u003cp\u003e3.2.1.3 Each-right \/: 62\u003c\/p\u003e \u003cp\u003e3.2.1.4 Cross Product \/: \\: 62\u003c\/p\u003e \u003cp\u003e3.2.1.5 Each-both ' 63\u003c\/p\u003e \u003cp\u003e3.2.2 Each-prior ': 66\u003c\/p\u003e \u003cp\u003e3.2.3 Compose (’) 67\u003c\/p\u003e \u003cp\u003e3.2.4 Over and Fold \/ 67\u003c\/p\u003e \u003cp\u003e3.2.5 Scan 68\u003c\/p\u003e \u003cp\u003e3.2.5.1 EMA: The Exponential Moving Average 69\u003c\/p\u003e \u003cp\u003e3.2.6 Converge 70\u003c\/p\u003e \u003cp\u003e3.2.6.1 Converge-repeat 70\u003c\/p\u003e \u003cp\u003e3.2.6.2 Converge-iterate 71\u003c\/p\u003e \u003cp\u003e3.3 Apply 72\u003c\/p\u003e \u003cp\u003e3.3.1 @ (apply) 72\u003c\/p\u003e \u003cp\u003e3.3.2 . (apply) 73\u003c\/p\u003e \u003cp\u003e3.4 Protected Evaluations 75\u003c\/p\u003e \u003cp\u003e3.5 Vector Operations 76\u003c\/p\u003e \u003cp\u003e3.5.1 Aggregators 76\u003c\/p\u003e \u003cp\u003e3.5.1.1 Simple Aggregators 76\u003c\/p\u003e \u003cp\u003e3.5.1.2 Weighted Aggregators 77\u003c\/p\u003e \u003cp\u003e3.5.2 Uniform Functions 77\u003c\/p\u003e \u003cp\u003e3.5.2.1 Running Functions 77\u003c\/p\u003e \u003cp\u003e3.5.2.2 Window Functions 78\u003c\/p\u003e \u003cp\u003e3.6 Convention for User-Defined Functions 79\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Editors and Other Tools 81\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Console 81\u003c\/p\u003e \u003cp\u003e4.2 Jupyter Notebook 82\u003c\/p\u003e \u003cp\u003e4.3 GUIs 84\u003c\/p\u003e \u003cp\u003e4.3.1 qStudio 85\u003c\/p\u003e \u003cp\u003e4.3.2 Q Insight Pad 88\u003c\/p\u003e \u003cp\u003e4.4 IDEs: IntelliJ IDEA 90\u003c\/p\u003e \u003cp\u003e4.5 Conclusion 92\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Debugging q Code 93\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction to Making It Wrong: Errors 93\u003c\/p\u003e \u003cp\u003e5.1.1 Syntax Errors 94\u003c\/p\u003e \u003cp\u003e5.1.2 Runtime Errors 94\u003c\/p\u003e \u003cp\u003e5.1.2.1 The Type Error 95\u003c\/p\u003e \u003cp\u003e5.1.2.2 Other Errors 98\u003c\/p\u003e \u003cp\u003e5.2 Debugging the Code 100\u003c\/p\u003e \u003cp\u003e5.3 Debugging Server-Side 102\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Two Data Operations\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Splayed and Partitioned Tables 107\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 107\u003c\/p\u003e \u003cp\u003e6.2 Saving a Table as a Single Binary File 108\u003c\/p\u003e \u003cp\u003e6.3 Splayed Tables 110\u003c\/p\u003e \u003cp\u003e6.4 Partitioned Tables 113\u003c\/p\u003e \u003cp\u003e6.5 Conclusion 119\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Joins 121\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Comma Operator 121\u003c\/p\u003e \u003cp\u003e7.2 Join Functions 125\u003c\/p\u003e \u003cp\u003e7.2.1 ij 125\u003c\/p\u003e \u003cp\u003e7.2.2 ej 126\u003c\/p\u003e \u003cp\u003e7.2.3 lj 126\u003c\/p\u003e \u003cp\u003e7.2.4 pj 127\u003c\/p\u003e \u003cp\u003e7.2.5 upsert 128\u003c\/p\u003e \u003cp\u003e7.2.6 uj 129\u003c\/p\u003e \u003cp\u003e7.2.7 aj 131\u003c\/p\u003e \u003cp\u003e7.2.8 aj0 134\u003c\/p\u003e \u003cp\u003e7.2.8.1 The Next Valid Join 135\u003c\/p\u003e \u003cp\u003e7.2.9 asof 138\u003c\/p\u003e \u003cp\u003e7.2.10 wj 140\u003c\/p\u003e \u003cp\u003e7.3 Advanced Example: Running TWAP 144\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Parallelisation 151\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Parallel Vector Operations 152\u003c\/p\u003e \u003cp\u003e8.2 Parallelisation over Processes 155\u003c\/p\u003e \u003cp\u003e8.3 Map-Reduce 155\u003c\/p\u003e \u003cp\u003e8.4 Advanced Topic: Parallel File\/Directory Access 158\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Data Cleaning and Filtering 161\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Predicate Filtering 161\u003c\/p\u003e \u003cp\u003e9.1.1 The Where Clause 161\u003c\/p\u003e \u003cp\u003e9.1.2 Aggregation Filtering 163\u003c\/p\u003e \u003cp\u003e9.2 Data Cleaning, Normalising and APIs 163\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 Parse Trees 165\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Definition 166\u003c\/p\u003e \u003cp\u003e10.1.1 Evaluation 166\u003c\/p\u003e \u003cp\u003e10.1.2 Parse Tree Creation 170\u003c\/p\u003e \u003cp\u003e10.1.3 Read-Only Evaluation 170\u003c\/p\u003e \u003cp\u003e10.2 Functional Queries 171\u003c\/p\u003e \u003cp\u003e10.2.1 Functional Select 174\u003c\/p\u003e \u003cp\u003e10.2.2 Functional Exec 178\u003c\/p\u003e \u003cp\u003e10.2.3 Functional Update 179\u003c\/p\u003e \u003cp\u003e10.2.4 Functional Delete 180\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11 A Few Use Cases 181\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Rolling VWAP 181\u003c\/p\u003e \u003cp\u003e11.1.1 N Tick VWAP 181\u003c\/p\u003e \u003cp\u003e11.1.2 TimeWindow VWAP 182\u003c\/p\u003e \u003cp\u003e11.2 Weighted Mid for N Levels of an Order Book 183\u003c\/p\u003e \u003cp\u003e11.3 Consecutive Runs of a Rule 185\u003c\/p\u003e \u003cp\u003e11.4 Real-Time Signals and Alerts 186\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Three Data Science\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12 Basic Overview of Statistics 191\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Histogram 191\u003c\/p\u003e \u003cp\u003e12.2 First Moments 196\u003c\/p\u003e \u003cp\u003e12.3 Hypothesis Testing 198\u003c\/p\u003e \u003cp\u003e12.3.1 Normal p-values 198\u003c\/p\u003e \u003cp\u003e12.3.2 Correlation 201\u003c\/p\u003e \u003cp\u003e12.3.2.1 Implementation 202\u003c\/p\u003e \u003cp\u003e12.3.3 t-test: One Sample 202\u003c\/p\u003e \u003cp\u003e12.3.3.1 Implementation 204\u003c\/p\u003e \u003cp\u003e12.3.4 t-test: Two Samples 204\u003c\/p\u003e \u003cp\u003e12.3.4.1 Implementation 205\u003c\/p\u003e \u003cp\u003e12.3.5 Sign Test 206\u003c\/p\u003e \u003cp\u003e12.3.5.1 Implementation of the Test 208\u003c\/p\u003e \u003cp\u003e12.3.5.2 Median Test 211\u003c\/p\u003e \u003cp\u003e12.3.6 Wilcoxon Signed-Rank Test 212\u003c\/p\u003e \u003cp\u003e12.3.7 Rank Correlation and Somers’ D 214\u003c\/p\u003e \u003cp\u003e12.3.7.1 Implementation 216\u003c\/p\u003e \u003cp\u003e12.3.8 Multiple Hypothesis Testing 221\u003c\/p\u003e \u003cp\u003e12.3.8.1 Bonferroni Correction 224\u003c\/p\u003e \u003cp\u003e12.3.8.2 Šidák’s Correction 224\u003c\/p\u003e \u003cp\u003e12.3.8.3 Holm’s Method 225\u003c\/p\u003e \u003cp\u003e12.3.8.4 Example 226\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13 Linear Regression 229\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Linear Regression 230\u003c\/p\u003e \u003cp\u003e13.2 Ordinary Least Squares 231\u003c\/p\u003e \u003cp\u003e13.3 The Geometric Representation of Linear Regression 233\u003c\/p\u003e \u003cp\u003e13.3.1 Moore–Penrose Pseudoinverse 235\u003c\/p\u003e \u003cp\u003e13.3.2 Adding Intercept 237\u003c\/p\u003e \u003cp\u003e13.4 Implementation of the OLS 240\u003c\/p\u003e \u003cp\u003e13.5 Significance of Parameters 243\u003c\/p\u003e \u003cp\u003e13.6 How Good is the Fit: R\u003csup\u003e2\u003c\/sup\u003e 244\u003c\/p\u003e \u003cp\u003e13.6.1 Adjusted R-squared 247\u003c\/p\u003e \u003cp\u003e13.7 Relationship with Maximum Likelihood Estimation and AIC with Small Sample Correction 248\u003c\/p\u003e \u003cp\u003e13.8 Estimation Suite 252\u003c\/p\u003e \u003cp\u003e13.9 Comparing Two Nested Models: Towards a Stopping Rule 254\u003c\/p\u003e \u003cp\u003e13.9.1 Comparing Two General Models 256\u003c\/p\u003e \u003cp\u003e13.10 In-\/Out-of-Sample Operations 257\u003c\/p\u003e \u003cp\u003e13.11 Cross-validation 262\u003c\/p\u003e \u003cp\u003e13.12 Conclusion 264\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 14 Time Series Econometrics 265\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Autoregressive and Moving Average Processes 265\u003c\/p\u003e \u003cp\u003e14.1.1 Introduction 265\u003c\/p\u003e \u003cp\u003e14.1.2 AR(p) Process 266\u003c\/p\u003e \u003cp\u003e14.1.2.1 Simulation 266\u003c\/p\u003e \u003cp\u003e14.1.2.2 Estimation of AR(p) Parameters 268\u003c\/p\u003e \u003cp\u003e14.1.2.3 Least Square Method 268\u003c\/p\u003e \u003cp\u003e14.1.2.4 Example 269\u003c\/p\u003e \u003cp\u003e14.1.2.5 Maximum Likelihood Estimator 269\u003c\/p\u003e \u003cp\u003e14.1.2.6 Yule-Walker Technique 269\u003c\/p\u003e \u003cp\u003e14.1.3 MA(q) Process 271\u003c\/p\u003e \u003cp\u003e14.1.3.1 Estimation of MA(q) Parameters 272\u003c\/p\u003e \u003cp\u003e14.1.3.2 Simulation 272\u003c\/p\u003e \u003cp\u003e14.1.3.3 Example 273\u003c\/p\u003e \u003cp\u003e14.1.4 ARMA(p, q) Process 273\u003c\/p\u003e \u003cp\u003e14.1.4.1 Invertibility of the ARMA(p, q) Process 274\u003c\/p\u003e \u003cp\u003e14.1.4.2 Hannan-Rissanen Algorithm: Two-Step Regression Estimation 274\u003c\/p\u003e \u003cp\u003e14.1.4.3 Yule-Walker Estimation 274\u003c\/p\u003e \u003cp\u003e14.1.4.4 Maximum Likelihood Estimation 275\u003c\/p\u003e \u003cp\u003e14.1.4.5 Simulation 275\u003c\/p\u003e \u003cp\u003e14.1.4.6 Forecast 276\u003c\/p\u003e \u003cp\u003e14.1.5 ARIMA(p, d, q) Process 276\u003c\/p\u003e \u003cp\u003e14.1.6 Code 276\u003c\/p\u003e \u003cp\u003e14.1.6.1 Simulation 277\u003c\/p\u003e \u003cp\u003e14.1.6.2 Estimation 278\u003c\/p\u003e \u003cp\u003e14.1.6.3 Forecast 282\u003c\/p\u003e \u003cp\u003e14.2 Stationarity and Granger Causality 285\u003c\/p\u003e \u003cp\u003e14.2.1 Stationarity 285\u003c\/p\u003e \u003cp\u003e14.2.2 Test of Stationarity – Dickey-Fuller and Augmented Dickey-Fuller Tests 286\u003c\/p\u003e \u003cp\u003e14.2.3 Granger Causality 286\u003c\/p\u003e \u003cp\u003e14.3 Vector Autoregression 287\u003c\/p\u003e \u003cp\u003e14.3.1 VAR(p) Process 288\u003c\/p\u003e \u003cp\u003e14.3.1.1 Notation 288\u003c\/p\u003e \u003cp\u003e14.3.1.2 Estimator 288\u003c\/p\u003e \u003cp\u003e14.3.1.3 Example 289\u003c\/p\u003e \u003cp\u003e14.3.1.4 Code 293\u003c\/p\u003e \u003cp\u003e14.3.2 VARX(p, q) Process 297\u003c\/p\u003e \u003cp\u003e14.3.2.1 Estimator 297\u003c\/p\u003e \u003cp\u003e14.3.2.2 Code 298\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 15 Fourier Transform 301\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Complex Numbers 301\u003c\/p\u003e \u003cp\u003e15.1.1 Properties of Complex Numbers 302\u003c\/p\u003e \u003cp\u003e15.2 Discrete Fourier Transform 308\u003c\/p\u003e \u003cp\u003e15.3 Addendum: Quaternions 314\u003c\/p\u003e \u003cp\u003e15.4 Addendum: Fractals 321\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 16 Eigensystem and PCA 325\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Theory 325\u003c\/p\u003e \u003cp\u003e16.2 Algorithms 327\u003c\/p\u003e \u003cp\u003e16.2.1 QR Decomposition 328\u003c\/p\u003e \u003cp\u003e16.2.2 QR Algorithm for Eigenvalues 330\u003c\/p\u003e \u003cp\u003e16.2.3 Inverse Iteration 331\u003c\/p\u003e \u003cp\u003e16.3 Implementation of Eigensystem Calculation 332\u003c\/p\u003e \u003cp\u003e16.3.1 QR Decomposition 333\u003c\/p\u003e \u003cp\u003e16.3.2 Inverse Iteration 337\u003c\/p\u003e \u003cp\u003e16.4 The Data Matrix and the Principal Component Analysis 341\u003c\/p\u003e \u003cp\u003e16.4.1 The Data Matrix 341\u003c\/p\u003e \u003cp\u003e16.4.2 PCA: The First Principal Component 344\u003c\/p\u003e \u003cp\u003e16.4.3 Second Principal Component 345\u003c\/p\u003e \u003cp\u003e16.4.4 Terminology and Explained Variance 347\u003c\/p\u003e \u003cp\u003e16.4.5 Dimensionality Reduction 349\u003c\/p\u003e \u003cp\u003e16.4.6 PCA Regression (PCR) 350\u003c\/p\u003e \u003cp\u003e16.5 Implementation of PCA 351\u003c\/p\u003e \u003cp\u003e16.6 Appendix: Determinant 354\u003c\/p\u003e \u003cp\u003e16.6.1 Theory 354\u003c\/p\u003e \u003cp\u003e16.6.2 Techniques to Calculate a Determinant 355\u003c\/p\u003e \u003cp\u003e16.6.3 Implementation of the Determinant 356\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 17 Outlier Detection 359\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 Local Outlier Factor 360\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 18 Simulating Asset Prices 369\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18.1 Stochastic Volatility Process with Price Jumps 369\u003c\/p\u003e \u003cp\u003e18.2 Towards the Numerical Example 371\u003c\/p\u003e \u003cp\u003e18.2.1 Numerical Preliminaries 371\u003c\/p\u003e \u003cp\u003e18.2.2 Implementing Stochastic Volatility Process with Jumps 374\u003c\/p\u003e \u003cp\u003e18.3 Conclusion 378\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Four Machine Learning\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 19 Basic Principles of Machine Learning 381\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e19.1 Non-Numeric Features and Normalisation 381\u003c\/p\u003e \u003cp\u003e19.1.1 Non-Numeric Features 381\u003c\/p\u003e \u003cp\u003e19.1.1.1 Ordinal Features 382\u003c\/p\u003e \u003cp\u003e19.1.1.2 Categorical Features 383\u003c\/p\u003e \u003cp\u003e19.1.2 Normalisation 383\u003c\/p\u003e \u003cp\u003e19.1.2.1 Normal Score 384\u003c\/p\u003e \u003cp\u003e19.1.2.2 Range Scaling 385\u003c\/p\u003e \u003cp\u003e19.2 Iteration: Constructing Machine Learning Algorithms 386\u003c\/p\u003e \u003cp\u003e19.2.1 Iteration 386\u003c\/p\u003e \u003cp\u003e19.2.2 Constructing Machine Learning Algorithms 389\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 20 Linear Regression with Regularisation 391\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e20.1 Bias–Variance Trade-off 392\u003c\/p\u003e \u003cp\u003e20.2 Regularisation 393\u003c\/p\u003e \u003cp\u003e20.3 Ridge Regression 394\u003c\/p\u003e \u003cp\u003e20.4 Implementation of the Ridge Regression 396\u003c\/p\u003e \u003cp\u003e20.4.1 Optimisation of the Regularisation Parameter 401\u003c\/p\u003e \u003cp\u003e20.5 Lasso Regression 403\u003c\/p\u003e \u003cp\u003e20.6 Implementation of the Lasso Regression 405\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 21 Nearest Neighbours 419\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e21.1 \u003ci\u003ek\u003c\/i\u003e-Nearest Neighbours Classifier 419\u003c\/p\u003e \u003cp\u003e21.2 Prototype Clustering 423\u003c\/p\u003e \u003cp\u003e21.3 Feature Selection: Local Nearest Neighbours Approach 429\u003c\/p\u003e \u003cp\u003e21.3.1 Implementation 430\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 22 Neural Networks 437\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e22.1 Theoretical Introduction 437\u003c\/p\u003e \u003cp\u003e22.1.1 Calibration 440\u003c\/p\u003e \u003cp\u003e22.1.1.1 Backpropagation 441\u003c\/p\u003e \u003cp\u003e22.1.2 The Learning Rate Parameter 443\u003c\/p\u003e \u003cp\u003e22.1.3 Initialisation 443\u003c\/p\u003e \u003cp\u003e22.1.4 Overfitting 444\u003c\/p\u003e \u003cp\u003e22.1.5 Dimension of the Hidden Layer(s) 444\u003c\/p\u003e \u003cp\u003e22.2 Implementation of Neural Networks 445\u003c\/p\u003e \u003cp\u003e22.2.1 Multivariate Encoder 445\u003c\/p\u003e \u003cp\u003e22.2.2 Neurons 446\u003c\/p\u003e \u003cp\u003e22.2.3 Training the Neural Network 448\u003c\/p\u003e \u003cp\u003e22.3 Examples 451\u003c\/p\u003e \u003cp\u003e22.3.1 Binary Classification 451\u003c\/p\u003e \u003cp\u003e22.3.2 M-class Classification 454\u003c\/p\u003e \u003cp\u003e22.3.3 Regression 457\u003c\/p\u003e \u003cp\u003e22.4 Possible Suggestions 463\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 23 AdaBoost with Stumps 465\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e23.1 Boosting 465\u003c\/p\u003e \u003cp\u003e23.2 Decision Stumps 466\u003c\/p\u003e \u003cp\u003e23.3 AdaBoost 467\u003c\/p\u003e \u003cp\u003e23.4 Implementation of AdaBoost 468\u003c\/p\u003e \u003cp\u003e23.5 Recommendation for Readers 474\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 24 Trees 477\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e24.1 Introduction to Trees 477\u003c\/p\u003e \u003cp\u003e24.2 Regression Trees 479\u003c\/p\u003e \u003cp\u003e24.2.1 Cost-Complexity Pruning 481\u003c\/p\u003e \u003cp\u003e24.3 Classification Tree 482\u003c\/p\u003e \u003cp\u003e24.4 Miscellaneous 484\u003c\/p\u003e \u003cp\u003e24.5 Implementation of Trees 485\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 25 Forests 495\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e25.1 Bootstrap 495\u003c\/p\u003e \u003cp\u003e25.2 Bagging 498\u003c\/p\u003e \u003cp\u003e25.2.1 Out-of-Bag 499\u003c\/p\u003e \u003cp\u003e25.3 Implementation 500\u003c\/p\u003e \u003cp\u003e25.3.1 Prediction 503\u003c\/p\u003e \u003cp\u003e25.3.2 Feature Selection 505\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 26 Unsupervised Machine Learning: The Apriori Algorithm 509\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e26.1 Apriori Algorithm 510\u003c\/p\u003e \u003cp\u003e26.2 Implementation of the Apriori Algorithm 511\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 27 Processing Information 523\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e27.1 Information Retrieval 523\u003c\/p\u003e \u003cp\u003e27.1.1 Corpus: Leonardo da Vinci 523\u003c\/p\u003e \u003cp\u003e27.1.2 Frequency Counting 524\u003c\/p\u003e \u003cp\u003e27.1.3 tf-idf 528\u003c\/p\u003e \u003cp\u003e27.2 Information as Features 532\u003c\/p\u003e \u003cp\u003e27.2.1 Sample: Simulated Proteins 533\u003c\/p\u003e \u003cp\u003e27.2.2 Kernels and Metrics for Proteins 535\u003c\/p\u003e \u003cp\u003e27.2.3 Implementation of Inner Products and Nearest Neighbours Principles 535\u003c\/p\u003e \u003cp\u003e27.2.4 Further Topics 539\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 28 Towards AI – Monte Carlo Tree Search 541\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e28.1 Multi-Armed Bandit Problem 541\u003c\/p\u003e \u003cp\u003e28.1.1 Analytic Solutions 543\u003c\/p\u003e \u003cp\u003e28.1.2 Greedy Algorithms 543\u003c\/p\u003e \u003cp\u003e28.1.3 Confidence-Based Algorithms 544\u003c\/p\u003e \u003cp\u003e28.1.4 Bayesian Algorithms 546\u003c\/p\u003e \u003cp\u003e28.1.5 Online Gradient Descent Algorithms 547\u003c\/p\u003e \u003cp\u003e28.1.6 Implementation of Some Learning Algorithms 547\u003c\/p\u003e \u003cp\u003e28.2 Monte Carlo Tree Search 558\u003c\/p\u003e \u003cp\u003e28.2.1 Selection Step 561\u003c\/p\u003e \u003cp\u003e28.2.2 Expansion Step 562\u003c\/p\u003e \u003cp\u003e28.2.3 Simulation Step 563\u003c\/p\u003e \u003cp\u003e28.2.4 Back Propagation Step 563\u003c\/p\u003e \u003cp\u003e28.2.5 Finishing the Algorithm 563\u003c\/p\u003e \u003cp\u003e28.2.6 Remarks and Extensions 564\u003c\/p\u003e \u003cp\u003e28.3 Monte Carlo Tree Search Implementation – Tic-tac-toe 565\u003c\/p\u003e \u003cp\u003e28.3.1 Random Games 566\u003c\/p\u003e \u003cp\u003e28.3.2 Towards the MCTS 570\u003c\/p\u003e \u003cp\u003e28.3.3 Case Study 579\u003c\/p\u003e \u003cp\u003e28.4 Monte Carlo Tree Search – Additional Comments 579\u003c\/p\u003e \u003cp\u003e28.4.1 Policy and Value Networks 579\u003c\/p\u003e \u003cp\u003e28.4.2 Reinforcement Learning 581\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 29 Econophysics: The Agent-Based Computational Models 583\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e29.1 Agent-Based Modelling 584\u003c\/p\u003e \u003cp\u003e29.1.1 Agent-Based Models in Society 584\u003c\/p\u003e \u003cp\u003e29.1.2 Agent-Based Models in Finance 586\u003c\/p\u003e \u003cp\u003e29.2 Ising Agent-Based Model for Financial Markets 587\u003c\/p\u003e \u003cp\u003e29.2.1 Ising Model in Physics 587\u003c\/p\u003e \u003cp\u003e29.2.2 Ising Model of Interacting Agents 587\u003c\/p\u003e \u003cp\u003e29.2.3 Numerical Implementation 588\u003c\/p\u003e \u003cp\u003e29.3 Conclusion 592\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 30 Epilogue: Art 595\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBibliography 601\u003c\/p\u003e \u003cp\u003eIndex 607\u003c\/p\u003e","brand":"John Wiley \u0026 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