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Introduction to Algorithms and Machine Learning: from Sorting to Strategic Agents

Introduction to Algorithms and Machine Learning: from Sorting to Strategic Agents
Author: Justin Skycak
Publisher: Justin Skycak
Total Pages: 424
Release: 2023-05-08
Genre: Computers
ISBN:
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This book was written to support Eurisko, an advanced math and computer science elective course sequence within the Math Academy program at Pasadena High School. During its operation from 2020 to 2023, Eurisko was the most advanced high school math/CS sequence in the USA. It culminated in high school students doing masters/PhD-level coursework (reproducing academic research papers in artificial intelligence, building everything from scratch in Python). CONTENTS 1. HELLO WORLD - Some Short Introductory Coding Exercises; Converting Between Binary, Decimal, and Hexadecimal; Recursive Sequences; Simulating Coin Flips; Roulette Wheel Selection; Cartesian Product. 2. SEARCHING AND SORTING - Brute Force Search with Linear-Encoding Cryptography; Solving Magic Squares via Backtracking; Estimating Roots via Bisection Search and Newton-Raphson Method; Single-Variable Gradient Descent; Multivariable Gradient Descent; Selection, Bubble, Insertion, and Counting Sort; Merge Sort and Quicksort. 3. OBJECTS - Basic Matrix Arithmetic; Reduced Row Echelon Form and Applications to Matrix Arithmetic; K-Means Clustering; Tic-Tac-Toe and Connect Four; Euler Estimation; SIR Model for the Spread of Disease; Hodgkin-Huxley Model of Action Potentials in Neurons; Hash Tables; Simplex Method. 4. REGRESSION AND CLASSIFICATION - Linear, Polynomial, and Multiple Linear Regression via Pseudoinverse; Regressing a Linear Combination of Nonlinear Functions via Pseudoinverse; Power, Exponential, and Logistic Regression via Pseudoinverse; Overfitting, Underfitting, Cross-Validation, and the Bias-Variance Tradeoff; Regression via Gradient Descent; Multiple Regression and Interaction Terms; K-Nearest Neighbors; Naive Bayes. 5. GRAPHS - Breadth-First and Depth-First Traversals; Distance and Shortest Paths in Unweighted Graphs; Dijkstra's Algorithm for Distance and Shortest Paths in Weighted Graphs; Decision Trees; Introduction to Neural Network Regressors; Backpropagation. 6. GAMES - Canonical and Reduced Game Trees for Tic-Tac-Toe; Minimax Strategy; Reduced Search Depth and Heuristic Evaluation for Connect Four; Introduction to Blondie24 and Neuroevolution; Reimplementing Fogel's Tic-Tac-Toe Paper; Reimplementing Blondie24; Reimplementing Blondie24: Convolutional Version.

A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence

A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence
Author: Nikos Vlassis
Publisher: Morgan & Claypool Publishers
Total Pages: 84
Release: 2007-06-01
Genre: Technology & Engineering
ISBN: 1598295276
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Multiagent systems is an expanding field that blends classical fields like game theory and decentralized control with modern fields like computer science and machine learning. This monograph provides a concise introduction to the subject, covering the theoretical foundations as well as more recent developments in a coherent and readable manner. The text is centered on the concept of an agent as decision maker. Chapter 1 is a short introduction to the field of multiagent systems. Chapter 2 covers the basic theory of singleagent decision making under uncertainty. Chapter 3 is a brief introduction to game theory, explaining classical concepts like Nash equilibrium. Chapter 4 deals with the fundamental problem of coordinating a team of collaborative agents. Chapter 5 studies the problem of multiagent reasoning and decision making under partial observability. Chapter 6 focuses on the design of protocols that are stable against manipulations by self-interested agents. Chapter 7 provides a short introduction to the rapidly expanding field of multiagent reinforcement learning. The material can be used for teaching a half-semester course on multiagent systems covering, roughly, one chapter per lecture.

Algorithms for Strategic Agents

Algorithms for Strategic Agents
Author: Seth Matthew Weinberg
Publisher:
Total Pages: 163
Release: 2014
Genre:
ISBN:
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In traditional algorithm design, no incentives come into play: the input is given, and your algorithm must produce a correct output. How much harder is it to solve the same problem when the input is not given directly, but instead reported by strategic agents with interests of their own? The unique challenge stems from the fact that the agents may choose to lie about the input in order to manipulate the behavior of the algorithm for their own interests, and tools from Game Theory are therefore required in order to predict how these agents will behave. We develop a new algorithmic framework with which to study such problems. Specifically, we provide a computationally efficient black-box reduction from solving any optimization problem on "strategic input," often called algorithmic mechanism design to solving a perturbed version of that same optimization problem when the input is directly given, traditionally called algorithm design. We further demonstrate the power of our framework by making significant progress on several long-standing open problems. First, we extend Myerson's celebrated characterization of single item auctions to multiple items, providing also a computationally efficient implementation of optimal auctions. Next, we design a computationally efficient 2-approximate mechanism for job scheduling on unrelated machines, the original problem studied in Nisan and Ronen's paper introducing the field of Algorithmic Mechanism Design. This matches the guarantee of the best known computationally efficient algorithm when the input is directly given. Finally, we provide the first hardness of approximation result for optimal mechanism design.

Introduction to Algorithms, fourth edition

Introduction to Algorithms, fourth edition
Author: Thomas H. Cormen
Publisher: MIT Press
Total Pages: 1313
Release: 2022-04-05
Genre: Computers
ISBN: 0262367505
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A comprehensive update of the leading algorithms text, with new material on matchings in bipartite graphs, online algorithms, machine learning, and other topics. Some books on algorithms are rigorous but incomplete; others cover masses of material but lack rigor. Introduction to Algorithms uniquely combines rigor and comprehensiveness. It covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers, with self-contained chapters and algorithms in pseudocode. Since the publication of the first edition, Introduction to Algorithms has become the leading algorithms text in universities worldwide as well as the standard reference for professionals. This fourth edition has been updated throughout. New for the fourth edition New chapters on matchings in bipartite graphs, online algorithms, and machine learning New material on topics including solving recurrence equations, hash tables, potential functions, and suffix arrays 140 new exercises and 22 new problems Reader feedback–informed improvements to old problems Clearer, more personal, and gender-neutral writing style Color added to improve visual presentation Notes, bibliography, and index updated to reflect developments in the field Website with new supplementary material Warning: Avoid counterfeit copies of Introduction to Algorithms by buying only from reputable retailers. Counterfeit and pirated copies are incomplete and contain errors.

Algorithms for Decision Making

Algorithms for Decision Making
Author: Mykel J. Kochenderfer
Publisher: MIT Press
Total Pages: 701
Release: 2022-08-16
Genre: Computers
ISBN: 0262047012
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A broad introduction to algorithms for decision making under uncertainty, introducing the underlying mathematical problem formulations and the algorithms for solving them. Automated decision-making systems or decision-support systems—used in applications that range from aircraft collision avoidance to breast cancer screening—must be designed to account for various sources of uncertainty while carefully balancing multiple objectives. This textbook provides a broad introduction to algorithms for decision making under uncertainty, covering the underlying mathematical problem formulations and the algorithms for solving them. The book first addresses the problem of reasoning about uncertainty and objectives in simple decisions at a single point in time, and then turns to sequential decision problems in stochastic environments where the outcomes of our actions are uncertain. It goes on to address model uncertainty, when we do not start with a known model and must learn how to act through interaction with the environment; state uncertainty, in which we do not know the current state of the environment due to imperfect perceptual information; and decision contexts involving multiple agents. The book focuses primarily on planning and reinforcement learning, although some of the techniques presented draw on elements of supervised learning and optimization. Algorithms are implemented in the Julia programming language. Figures, examples, and exercises convey the intuition behind the various approaches presented.

Reinforcement Learning, second edition

Reinforcement Learning, second edition
Author: Richard S. Sutton
Publisher: MIT Press
Total Pages: 549
Release: 2018-11-13
Genre: Computers
ISBN: 0262352702
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The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.

An Introduction to Agent-Based Modeling

An Introduction to Agent-Based Modeling
Author: Uri Wilensky
Publisher: MIT Press
Total Pages: 505
Release: 2015-04-03
Genre: Computers
ISBN: 0262731894
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A comprehensive and hands-on introduction to the core concepts, methods, and applications of agent-based modeling, including detailed NetLogo examples. The advent of widespread fast computing has enabled us to work on more complex problems and to build and analyze more complex models. This book provides an introduction to one of the primary methodologies for research in this new field of knowledge. Agent-based modeling (ABM) offers a new way of doing science: by conducting computer-based experiments. ABM is applicable to complex systems embedded in natural, social, and engineered contexts, across domains that range from engineering to ecology. An Introduction to Agent-Based Modeling offers a comprehensive description of the core concepts, methods, and applications of ABM. Its hands-on approach—with hundreds of examples and exercises using NetLogo—enables readers to begin constructing models immediately, regardless of experience or discipline. The book first describes the nature and rationale of agent-based modeling, then presents the methodology for designing and building ABMs, and finally discusses how to utilize ABMs to answer complex questions. Features in each chapter include step-by-step guides to developing models in the main text; text boxes with additional information and concepts; end-of-chapter explorations; and references and lists of relevant reading. There is also an accompanying website with all the models and code.

Introduction to Algorithms & Data Structures 1

Introduction to Algorithms & Data Structures 1
Author: Bolakale Aremu
Publisher: Introduction to Algorithms & Data Structures
Total Pages: 0
Release: 2023-06
Genre:
ISBN: 9781088153642
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Benefits of This Book. Learning algorithms and data structures from this book will help you become a better programmer. Algorithms and data structures will make you think more logically. Furthermore, they can help you design better systems for storing and processing data. They also serve as a tool for optimization and problem-solving. As a result, the concepts of algorithms and data structures are very valuable in any field. For example, you can use them when building a web app or writing software for other devices. You can apply them to machine learning and data analytics, which are two hot areas right now. If you are a hacker, algorithms and data structures in Python are also important for you everywhere. Now, whatever your preferred learning style, I've got you covered. If you're a visual learner, you'll love my clear diagrams and illustrations throughout this book. If you're a practical learner, you'll love my hands-on lessons so that you can get practical with algorithms and data structures and learn in a hands-on way. Course Structure. There are three volumes in this course. This is volume one. In this volume, you'll take a deep dive into the world of algorithms. With increasing frequency, algorithms are starting to shape our lives in many ways - from the products recommended to us, to the friends we interact with on social media, to even important social issues like policing, privacy and healthcare. So, the first part of this course covers what algorithms are, how they work, and where they can be found (real life applications). In the second volume, you'll work through an introduction to data structures. You're going to learn about two introductory data structures - arrays and linked lists. You'll look at common operations and how the runtimes of these operations affect our everyday code. In the third volume, you're going to bring your knowledge of algorithms and data structures together to solve the problem of sorting data using the Merge Sort algorithm. In this volume, we will look at algorithms in two categories: sorting and searching. You'll implement well-known sorting algorithms like Selection Sort, Quicksort, and Merge Sort. You'll also learn basic search algorithms like Sequential Search and Binary Search. At the end of many sections of this course, short practice exercises are provided to test your understanding of the topic discussed. Answers are also provided so you can check how well you have performed in each section. At the end of the course, assessment tests are provided. You will also find links to download more helpful resources such as codes and screenshots used in this book, and more practice exercises. You can use them for quick references and revision as well. My support link is also provided so you to contact me any time if you have questions or need further help. By the end of this course, you will understand what algorithms and data structures are, how they are measured and evaluated, and how they are used to solve real-life problems. So, everything you need is right here in this course. I really hope you'll enjoy it. Are you ready? Let's dive in!

Introduction to Algorithms & Data Structures 2

Introduction to Algorithms & Data Structures 2
Author: Bolakale Aremu
Publisher: Introduction to Algorithms & Data Structures
Total Pages: 0
Release: 2023-06
Genre:
ISBN: 9781088104026
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Benefits of This Book Learning algorithms and data structures from this book will help you become a better programmer. Algorithms and data structures will make you think more logically. Furthermore, they can help you design better systems for storing and processing data. They also serve as a tool for optimization and problem-solving. As a result, the concepts of algorithms and data structures are very valuable in any field. For example, you can use them when building a web app or writing software for other devices. You can apply them to machine learning and data analytics, which are two hot areas right now. If you are a hacker, algorithms and data structures in Python are also important for you everywhere. Now, whatever your preferred learning style, I've got you covered. If you're a visual learner, you'll love my clear diagrams and illustrations throughout this book. If you're a practical learner, you'll love my hands-on lessons so that you can get practical with algorithms and data structures and learn in a hands-on way. Course Structure There are three volumes in this course. This is volume two. In volume one, I took a deep dive into the world of algorithms. I covered what algorithms are, how they work, and where they can be found (real life applications). In this part of the series (volume two), we'll work through an introduction to data structures. You're going to learn about two introductory data structures - arrays and linked lists. You'll look at common operations and how the runtimes of these operations affect our everyday code. In the third volume, you're going to bring your knowledge of algorithms and data structures together to solve the problem of sorting data using the Merge Sort algorithm. We will look at algorithms in two categories: sorting and searching. You'll implement well-known sorting algorithms like Selection Sort, Quicksort, and Merge Sort. You'll also learn basic search algorithms like Sequential Search and Binary Search. At the end of many sections of this course, short practice exercises are provided to test your understanding of the topic discussed. Answers are also provided so you can check how well you have performed in each section. At the end of the course, you will find a link to download more helpful resources, such as codes and screenshots used in this book, and more practice exercises. You can use them for quick references and revision as well. My support link is also provided so you to contact me any time if you have questions or need further help. By the end of this course, you will understand what algorithms and data structures are, how they are measured and evaluated, and how they are used to solve real-life problems. So, everything you need is right here in this book. I really hope you'll enjoy it. Are you ready? Let's dive in!

Hands-On Machine Learning for Algorithmic Trading

Hands-On Machine Learning for Algorithmic Trading
Author: Stefan Jansen
Publisher: Packt Publishing Ltd
Total Pages: 668
Release: 2018-12-31
Genre: Computers
ISBN: 1789342716
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Explore effective trading strategies in real-world markets using NumPy, spaCy, pandas, scikit-learn, and Keras Key FeaturesImplement machine learning algorithms to build, train, and validate algorithmic modelsCreate your own algorithmic design process to apply probabilistic machine learning approaches to trading decisionsDevelop neural networks for algorithmic trading to perform time series forecasting and smart analyticsBook Description The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This book enables you to use a broad range of supervised and unsupervised algorithms to extract signals from a wide variety of data sources and create powerful investment strategies. This book shows how to access market, fundamental, and alternative data via API or web scraping and offers a framework to evaluate alternative data. You'll practice the ML workflow from model design, loss metric definition, and parameter tuning to performance evaluation in a time series context. You will understand ML algorithms such as Bayesian and ensemble methods and manifold learning, and will know how to train and tune these models using pandas, statsmodels, sklearn, PyMC3, xgboost, lightgbm, and catboost. This book also teaches you how to extract features from text data using spaCy, classify news and assign sentiment scores, and to use gensim to model topics and learn word embeddings from financial reports. You will also build and evaluate neural networks, including RNNs and CNNs, using Keras and PyTorch to exploit unstructured data for sophisticated strategies. Finally, you will apply transfer learning to satellite images to predict economic activity and use reinforcement learning to build agents that learn to trade in the OpenAI Gym. What you will learnImplement machine learning techniques to solve investment and trading problemsLeverage market, fundamental, and alternative data to research alpha factorsDesign and fine-tune supervised, unsupervised, and reinforcement learning modelsOptimize portfolio risk and performance using pandas, NumPy, and scikit-learnIntegrate machine learning models into a live trading strategy on QuantopianEvaluate strategies using reliable backtesting methodologies for time seriesDesign and evaluate deep neural networks using Keras, PyTorch, and TensorFlowWork with reinforcement learning for trading strategies in the OpenAI GymWho this book is for Hands-On Machine Learning for Algorithmic Trading is for data analysts, data scientists, and Python developers, as well as investment analysts and portfolio managers working within the finance and investment industry. If you want to perform efficient algorithmic trading by developing smart investigating strategies using machine learning algorithms, this is the book for you. Some understanding of Python and machine learning techniques is mandatory.