Risk And Ai (rai): Garp Prep Course

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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.



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