Combining data, AI, network science, and psychology for social influence. What you'll learn How cognitive biases mold our view of the world, and how they can be leveraged to exert influence How directed influence campaigns shape opinion in social networks How AI can generate realistic data, and how that data can be used to deceive How to build graph neural networks (GNN, GCN, GAT, Node2Vec, DeepWalk, & more) How statistical analysis and hypothesis tests can be fudged to accept or reject any hypothesis How to detect rising stars in social networks and root out botnets Build a hate speech detector bot for Slack Build a news recommendation website Run Bayesian A/B tests in real time on your news recommendation website Requirements You should know the foundations of machine learning, statistics, and network science. Intermediate Python and Docker skills are required for the projects. You should know how to use the following libraries: Numpy, Pytorch, Django, FastAPI Some knowledge of linear algebra, psychology, and philosophy would be helpful. Description A new age has arrived. AI is sufficiently advanced to learn our opinions and what we care about, and craft text and media to influence our thoughts and opinions. It is likely that AI will soon be better able to influence us than other people. Individuals and organizations equipped with AI are now able to exert influence at a previously inconceivable scale, and they will become more successful at it over time.In this course, we will combine concepts from psychology, data science, and network science to describe how social influence can be exerted. We will consider how our thoughts are influenced by our social networks, and how our biases work. We will explore how an individual’s opinions impact social networks, and how the collective opinions of entire networks can change under the right conditions. You will see how statistical analysis can be manipulated and how AI can be used for deception. Ultimately, you will learn how to exert large scale social influence, using AI for leverage.This is not a course for beginners. Basic concepts in data science will not be explained. This is an interdisciplinary course that will challenge you to think for yourself. You will learn about powerful techniques and you will need to decide how to manage them ethically and morally. Overview Section 1: Introduction Lecture 1 About this Course Lecture 2 Are You Ready for this Course? Lecture 3 Course Materials Section 2: Psychology of Social Influence Lecture 4 Psychology of Social Influence Intro Lecture 5 A Perspective of Social Influence Lecture 6 Cognitive Biases Part 1: Primers, Illusory Truth Effect, Availability Heuristic Lecture 7 Cognitive Biases Part 2: Cognitive Dissonance Lecture 8 Cognitive Biases Part 3: Conformity & Ostracism Lecture 9 Behavior in Groups Section 3: Influence in Social Networks Lecture 10 Influence in Social Networks Intro Lecture 11 Influence Lecture 12 Influence Decay and the Network Horizon Lecture 13 Information Spread in Social Networks Lecture 14 Phase Transitions in the Ising Model Lecture 15 The Rise of an Influencer + Demo of Detecting a Rising Star Section 4: Graph Representation Learning Lecture 16 Graph Representation Learning Intro Lecture 17 Graph Feature Engineering Lecture 18 Graph Spectral Properties & the Laplacian Lecture 19 Graph Embeddings Lecture 20 GNNs Part 1 Lecture 21 GNNs Part 2 Lecture 22 Graph Convolutions & GCNs Lecture 23 Graph Embeddings & GNNs for Dynamic Graphs Lecture 24 Evaluating Graph Representations Lecture 25 Project Overview: Node Classification with GNNs Lecture 26 Project: Node Classification with GNNs Section 5: Data Manipulation Lecture 27 Data Manipulation Intro Lecture 28 How to Fake Statistical Analysis Lecture 29 Bayesian A/B Testing Lecture 30 How to Generate Realistic Data Lecture 31 Demo: How to Break Benford's Law Lecture 32 Fake News & Deepfakes Lecture 33 How to Create a Deepfake & Leverage it for Social Influence Lecture 34 Exploiting Data Visualization Section 6: Media Bias & Propaganda Lecture 35 Media Bias & Propaganda Intro Lecture 36 Media Bias Lecture 37 Propaganda Lecture 38 Censorship Lecture 39 Project Overview: Hate Speech Detection Lecture 40 Project: Hate Speech Detector Lecture 41 Project Overview: News Recommender Lecture 42 Project: News Recommender Section 7: Directed Influence Campaigns & Botnets Lecture 43 Directed Influence Campaigns Intro Lecture 44 Directed Influence Lecture 45 Demo: Social Botnet Detection Lecture 46 Project Overview: Directed Influence Campaign Lecture 47 Project: Directed Influence Campaign Section 8: Conclusion Lecture 48 Where to Go From Here Data Scientists, ML Engineers, and Data Analysts with a few years of work experience or higher education,This is not a course for beginners.
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