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    Risk And Ai (Rai): Garp Prep Course

    Posted By: ELK1nG
    Risk And Ai (Rai): Garp Prep Course

    Risk And Ai (Rai): Garp Prep Course
    Published 5/2025
    MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
    Language: English | Size: 6.07 GB | Duration: 18h 59m

    Master the GARP Risk and AI Certification: Understand AI risks, governance, and applications in finance

    What you'll learn

    Understand the foundational concepts of Artificial Intelligence and Machine Learning

    Analyze and evaluate the risks associated with AI models

    Apply governance and risk management frameworks

    Prepare effectively for the GARP Risk and AI Certification exam

    Requirements

    No prior experience with AI or risk management is required

    A basic understanding of finance or risk concepts

    Familiarity with business or technology terminology used in financial services will enhance the learning experience but is not a strict prerequisite.

    Description

    Are you ready to future-proof your career at the intersection of finance, risk management, and artificial intelligence?This course is your ultimate companion to prepare for the GARP Risk and AI (RAI) Certification—the world’s first global certification designed to equip professionals with a deep understanding of AI risks, governance, and regulatory expectations in the financial services industry.Whether you're a risk manager trying to keep pace with emerging technologies, a data scientist navigating model governance, a compliance officer concerned with responsible AI use, or student freshly embarked on an AI journey, this course is built for you.Through concise lessons, real-world case studies, practice questions, and exam-oriented guidance, you’ll gain:A strong grasp of AI/ML fundamentals tailored for financePractical insights into identifying, measuring, and mitigating AI-related risksFrameworks for ethical AI, model validation, and regulatory complianceA strategic study plan aligned with GARP’s official RAI syllabusNo prior technical or AI background? No problem. This course breaks down complex concepts into clear, actionable knowledge.Join now and take a confident step toward becoming a future-ready risk professional with GARP’s Risk and AI Certification. This course is taught by professionals working in the AI domain and have thousands of students across more than 100 countries!

    Overview

    Section 1: Welcome and Overview

    Lecture 1 Course Overview

    Lecture 2 Practice Test

    Lecture 3 Join the Community for Live Classes and Q&A Sessions

    Section 2: Module I

    Lecture 4 Classical AI

    Lecture 5 Specific Vs General AI

    Lecture 6 Good Old Fashioned AI (GOFAI)

    Lecture 7 Simple Reinforcement Learning

    Lecture 8 Lookahead

    Lecture 9 Search in AI

    Lecture 10 Recursion

    Lecture 11 Recursive Adversarial Tree Search in AI

    Lecture 12 Complexity, Heuristics, and Reinforcement Learning

    Lecture 13 Limits of Classical AI

    Lecture 14 Introducing Neural Nets

    Lecture 15 Artificial Neuron

    Lecture 16 Connectionism and Its Early Challenges

    Lecture 17 Deep Learning

    Lecture 18 DL Beats Symbolic AI at Its Own Game

    Lecture 19 Inscrutability of Deep Learning

    Lecture 20 Dawn of AGI

    Lecture 21 ML & Risks

    Lecture 22 Examples of Unsupervised Learning - PCA

    Lecture 23 Risks of Inscrutability

    Lecture 24 Risks of Overreliance

    Lecture 25 Risks to Us

    Section 3: Module 2 - Chapter 1: Intro to Tools

    Lecture 26 Introduction

    Lecture 27 Types of ML

    Lecture 28 Exploratory Data Analysis

    Lecture 29 Data Cleaning

    Lecture 30 Data Visualization

    Lecture 31 Feature Extraction

    Lecture 32 Data Scaling

    Lecture 33 Data Transformation

    Lecture 34 Dimensionality Reduction Techniques

    Lecture 35 Training, Validation, and Testing

    Lecture 36 Software for Machine Learning

    Section 4: Module 2: Chapter 2 - Unsupervised Learning

    Lecture 37 Introduction

    Lecture 38 K-Means Algorithm

    Lecture 39 Performance Management

    Lecture 40 Selecting Centroids

    Lecture 41 Selection of Centroids - Example

    Lecture 42 Advantages and Problems of K-Means

    Lecture 43 Fuzzy K-Means

    Lecture 44 Hierarchical Clustering

    Lecture 45 Density Based Clustering

    Section 5: Module 2 - Chapter 3: Simple Linear Regression

    Lecture 46 Introduction: Simple Linear Regression

    Lecture 47 Multi Linear Regression

    Lecture 48 Wage Rates Example

    Lecture 49 Potential Problems in Regression

    Lecture 50 Stepwise Regression Procedure

    Lecture 51 Classification Problem

    Lecture 52 Other Types of Limited Dependent Variable Models

    Lecture 53 Linear Discriminant Analysis

    Section 6: Module 2 - Chapter 4: Supervised Learning - Part II

    Lecture 54 Introduction

    Lecture 55 Regression Trees

    Lecture 56 Classification Trees

    Lecture 57 Pruning

    Lecture 58 Ensemble Methods

    Lecture 59 K-Nearest Neighbors

    Lecture 60 Support Vector Machines

    Lecture 61 SVM Example and Extensions

    Lecture 62 Neural Networks

    Lecture 63 Choice of Activation Function

    Lecture 64 Numerical Example

    Lecture 65 Backpropagation

    Lecture 66 Architectural Issues

    Lecture 67 Overfitting

    Lecture 68 Advanced Neural Network Structures

    Lecture 69 Autoencoders

    Section 7: Module 2 - Chapter 5: Semi-Supervised Learning

    Lecture 70 Introdution

    Lecture 71 Techniques

    Lecture 72 Self-Training

    Lecture 73 Co-Training

    Lecture 74 Unsupervised Preprocessing

    Section 8: Module 2: Chapter 6 - Reinforcement Learning

    Lecture 75 Intro to RL

    Lecture 76 Multi-Arm Bandit

    Lecture 77 Strategies in RL

    Lecture 78 Markov Decision Process

    Lecture 79 Approaches to RL

    Lecture 80 The Bellman Equations

    Section 9: Module 2: Chapter 7 - Supervised Learning - Model Estimation

    Lecture 81 Ordinary Least Squares

    Lecture 82 Non Linear Squares

    Lecture 83 Hill Climbing

    Lecture 84 The Gradient Descent Method

    Lecture 85 Backpropagation

    Lecture 86 Computational Issues

    Lecture 87 Maximum Likelihood

    Lecture 88 Overfitting

    Lecture 89 Underfitting

    Lecture 90 Bias-variance Trade Off

    Lecture 91 Prediction Accuracy Versus Interpretability

    Lecture 92 Regularization - Ridge Regression

    Lecture 93 LASSO

    Lecture 94 Elastic Net

    Lecture 95 Regularization Example

    Lecture 96 Cross Validation

    Lecture 97 Stratified Cross-validation

    Lecture 98 Bootstrapping

    Lecture 99 Grid Searches

    Section 10: Module 2: Chapter 8 - Supervised Learning - Model Performance Evaluation

    Lecture 100 Introduction - Model Evaluation

    Lecture 101 Model Performance Evaluation - Continuous Variable

    Lecture 102 Classification Model Prediction

    Lecture 103 Model Performance Evaluation - Classification

    Section 11: Module 2: Chapter 9 - NLP

    Lecture 104 Introduction

    Lecture 105 Data Preprocessing

    Lecture 106 NLP Models

    Lecture 107 Vector Normalization

    Lecture 108 Dictionary Comparison Approaches

    Lecture 109 N Grams

    Lecture 110 TF-IDF

    Lecture 111 ML Approaches

    Lecture 112 Naive Bayes

    Lecture 113 Word Meaning

    Lecture 114 NLP Evaluation

    Section 12: Module 2: Chapter 10 - Generative AI

    Lecture 115 Intro - GenAI

    Lecture 116 Intro - Word Embeddings, Word2Vec, RNNs

    Lecture 117 Word2Vec

    Lecture 118 RNNs

    Lecture 119 Transformers and LLMs

    Lecture 120 LLMs

    Lecture 121 Early LLMs

    Lecture 122 Cloud-Based LLMs

    Lecture 123 Evolution of GenAI

    Section 13: Module 3 - Risk and Risk Factors

    Lecture 124 Introduction

    Lecture 125 Bias and Fairness

    Lecture 126 Group Fairness

    Lecture 127 Individual Fairness

    Lecture 128 Demographic Parity

    Lecture 129 Confusion Matrix

    Lecture 130 Predictive Rate Parity

    Lecture 131 Impossibility and Trade Offs

    Lecture 132 Equal Opportunities

    Lecture 133 Sources of Unfairness

    Lecture 134 Data Collection and Composition

    Lecture 135 Model Development

    Lecture 136 Model Development

    Lecture 137 Explainability, Interpretability, and Transparency

    Lecture 138 Black Box Problem

    Lecture 139 Opaqueness

    Lecture 140 Explainable AI (XAI)

    Lecture 141 Autonomy and Manipulation

    Lecture 142 Safety and Well-Being

    Lecture 143 Reputational Risks

    Lecture 144 Existential Risks

    Lecture 145 Global Challenges and Risks

    Lecture 146 Misinformation Campaigns

    Section 14: Module 4

    Lecture 147 Introduction - Responsible and Ethical AI

    Lecture 148 Practical Ethics

    Lecture 149 Ethical Frameworks

    Lecture 150 Deontology

    Lecture 151 Virtue Ethics

    Lecture 152 What can AI Ethics learn from Medical Ethics

    Lecture 153 Principles of AI Ethics

    Lecture 154 Bias and Discrimination

    Lecture 155 Fairness in AI Systems

    Lecture 156 Privacy and Cybersecurity

    Lecture 157 Governance Challenges

    Lecture 158 GC 2: Lack of AI Ethics Structures, Lack of Regulations

    Lecture 159 GC 3: Unpredictability Issues, Lack of Truth Tracking Abilities, & Privacy

    Section 15: Module 5: Data and AI Model Governance

    Lecture 160 Intro - Data and AI Model Governance

    Lecture 161 Data Governance

    Lecture 162 Data Provenance

    Lecture 163 Data Classification and Metadata Management

    Lecture 164 Data Protection, Security, & Compliance

    Lecture 165 Board Roles and Responsibilities

    Lecture 166 Model Governance

    Lecture 167 Model Development and Testing

    Lecture 168 Testing Responsibilities

    Lecture 169 Use Test in QRM

    Lecture 170 Model Validation in QRMs

    Lecture 171 Model Governance Policies

    Lecture 172 Model Inventory and Landscape

    Lecture 173 Model Validation Overview

    Lecture 174 Model Design

    Lecture 175 Numerical and Statistical Issues - Discretization

    Lecture 176 Approximation

    Lecture 177 Numerical Evaluation in QRMs

    Lecture 178 Random Numbers

    Lecture 179 Implementation, Software, and Data

    Lecture 180 Processes and Misinterpretation I

    Lecture 181 Processes and Misinterpretation II

    Finance and risk professionals seeking to understand how AI is transforming risk management and aiming to earn the GARP Risk and AI (RAI) certification.,Compliance officers, auditors, and regulators who need a structured understanding of the risks and governance challenges posed by AI-driven systems in financial institutions.,Students, career switchers, and early-career professionals interested in entering the intersection of finance, risk, and emerging technologies—no prior AI or deep finance knowledge required.