ISO 24029 — Assessment of Robustness of Neural Networks — Certification Brochure

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London Academy of Professional Training
Certificate
ISO 24029 — Assessment of Robustness of Neural Networks
ISO Certification Programme
REF: IIT-AII-24029  •  ISO IT & Related Technologies  •  Artificial Intelligence
6Subjects 25Chapters 150Classes 430Total Marks 60%Pass Mark LifetimeValidity
At a Glance
ReferenceIIT-AII-24029
AwardCertificate
Assessment430 marks — 258 to pass (60%)
PrerequisitesNone
ValidityLifetime
Awarding BodyLAPT — London Academy of Professional Training
Curriculum
1 Assessment Techniques for AI Systems 5 chapters · 30 classes  105 marks
• 1 Foundations of AI System Assessment Techniques — 6 classes
› 1.1 Identify Key Components of AI System Assessment
› 1.2 Analyze Assessment Techniques for AI Robustness
› 1.3 Compare Qualitative and Quantitative Assessment Methods
› 1.4 Design a Framework for Evaluating AI Systems
› 1.5 Implement Assessment Metrics for Neural Network Robustness
› 1.6 Reflect on the Impact of Assessment Techniques on AI Development
• 2 Evaluating Neural Network Architectures — 6 classes
› 2.1 Understand Key Characteristics of Neural Network Architectures
› 2.2 Explore Common Neural Network Models and Their Applications
› 2.3 Assess Performance Metrics for Neural Networks
› 2.4 Analyze Strengths and Weaknesses of Various Architectures
› 2.5 Apply Evaluation Techniques to Compare Neural Network Designs
› 2.6 Develop a Framework for Robustness Assessment in Neural Networks
• 3 Robustness Metrics and Measurement Methods — 6 classes
› 3.1 Identify Key Robustness Metrics for Neural Networks
› 3.2 Analyze Measurement Methods for Evaluating Robustness
› 3.3 Explore Statistical Techniques for Robustness Assessment
› 3.4 Compare Different Robustness Metrics in Real-world Scenarios
› 3.5 Apply Robustness Measurement Methods to Sample AI Systems
› 3.6 Review Best Practices for Reporting Robustness Results
• 4 Testing Strategies for AI System Resilience — 6 classes
› 4.1 Identify Key Concepts in AI Resilience Assessment
› 4.2 Explore Various Testing Strategies for Neural Networks
› 4.3 Analyze Effectiveness of Stress Testing Techniques
› 4.4 Implement Adversarial Testing to Evaluate Robustness
› 4.5 Develop a Framework for Continuous Testing in AI Systems
› 4.6 Create a Comprehensive Report on AI System Resilience Findings
• 5 Future Trends in AI Robustness Assessment — 6 classes
› 5.1 Explore Emerging Frameworks for AI Robustness Assessment
› 5.2 Analyze Current Trends Influencing AI System Security
› 5.3 Identify Key Metrics for Evaluating Neural Network Resilience
› 5.4 Examine Case Studies on Robustness Failures in AI
› 5.5 Develop Strategies for Implementing Robustness Assessments
› 5.6 Reflect on Future Challenges and Opportunities in AI Robustness
2 Data Analysis and Interpretations 5 chapters · 30 classes  65 marks
• 1 Fundamentals of Data Analysis in Neural Networks — 6 classes
› 1.1 Define Key Concepts of Data Analysis in Neural Networks
› 1.2 Identify Different Data Types Used in Neural Networks
› 1.3 Explore Data Preprocessing Techniques for Machine Learning
› 1.4 Analyze the Role of Training Data in Neural Network Performance
› 1.5 Evaluate Model Performance Metrics for Neural Network Assessment
› 1.6 Apply Data Visualization Techniques to Interpret Neural Network Results
• 2 Statistical Methods for Data Interpretation — 6 classes
› 2.1 Define Key Statistical Concepts for Data Interpretation
› 2.2 Apply Descriptive Statistics to Summarize Data Sets
› 2.3 Utilize Probability Distributions in Data Analysis
› 2.4 Conduct Hypothesis Testing for Statistical Inference
› 2.5 Analyze Correlation and Regression Techniques
› 2.6 Implement Statistical Methods for Real-World Data Interpretation
• 3 Validation Techniques for Robustness Assessment — 6 classes
› 3.1 Identify Key Validation Techniques for Neural Networks
› 3.2 Analyze Metrics for Evaluating Model Robustness
› 3.3 Compare Cross-Validation Methods for Robust Assessment
› 3.4 Implement Data Augmentation for Enhanced Validation
› 3.5 Examine the Impact of Adversarial Testing on Robustness
› 3.6 Apply Robustness Assessment Techniques in Real-World Scenarios
• 4 Data Visualization and Reporting Results — 6 classes
› 4.1 Identify Key Elements of Effective Data Visualizations
› 4.2 Explore Different Types of Data Visualizations and Their Uses
› 4.3 Analyze the Impact of Color and Design on Data Interpretation
› 4.4 Develop Interactive Dashboards for Dynamic Data Presentation
› 4.5 Construct Comprehensive Reports that Communicate Findings Clearly
› 4.6 Present Data Insights to Stakeholders Using Visual Tools
• 5 Advanced Analytical Techniques for Neural Network Optimization — 6 classes
› 5.1 Analyze Neural Network Performance Metrics
› 5.2 Evaluate Data Preprocessing Techniques for Optimization
› 5.3 Implement Regularization Methods to Prevent Overfitting
› 5.4 Explore Hyperparameter Tuning Strategies
› 5.5 Apply Advanced Optimization Algorithms
› 5.6 Assess Model Robustness through Validation Techniques
3 Designing Resilient Neural Networks 5 chapters · 30 classes  65 marks
• 1 Understanding Neural Network Architectures and Components — 6 classes
› 1.1 Explore Key Components of Neural Networks
› 1.2 Identify Types of Neural Network Architectures
› 1.3 Analyze the Role of Activation Functions
› 1.4 Examine the Importance of Layers in Neural Networks
› 1.5 Evaluate Different Training Techniques for Resilience
› 1.6 Design a Simple Neural Network Architecture
• 2 Principles of Robustness in Neural Networks — 6 classes
› 2.1 Define Robustness in Neural Networks
› 2.2 Identify Challenges to Neural Network Robustness
› 2.3 Explore Techniques for Enhancing Robustness
› 2.4 Evaluate Robustness through Testing Methods
› 2.5 Analyze Case Studies of Robust Neural Networks
› 2.6 Implement Best Practices for Resilience in Design
• 3 Techniques for Enhancing Neural Network Robustness — 6 classes
› 3.1 Identify Key Vulnerabilities in Neural Networks
› 3.2 Explore Regularization Techniques for Enhanced Generalization
› 3.3 Implement Data Augmentation Strategies for Robust Training
› 3.4 Analyze Adversarial Training Methods to Improve Resilience
› 3.5 Evaluate Ensemble Techniques for Enhanced Network Performance
› 3.6 Apply Robustness Metrics to Assess Neural Network Strength
• 4 Testing and Evaluating Neural Network Robustness — 6 classes
› 4.1 Identify Key Metrics for Neural Network Robustness
› 4.2 Explore Common Testing Techniques for Neural Networks
› 4.3 Implement Adversarial Testing Scenarios
› 4.4 Analyze Performance Under Varying Conditions
› 4.5 Conduct Comparative Evaluations of Robustness Strategies
› 4.6 Develop a Comprehensive Robustness Assessment Report
• 5 Real-World Applications and Case Studies of Resilient Neural Networks — 6 classes
› 5.1 Analyze the Importance of Resilient Neural Networks in Industry
› 5.2 Explore Case Studies of Neural Networks in Healthcare Applications
› 5.3 Examine the Role of Resilience in Autonomous Vehicles
› 5.4 Investigate Financial Sector Applications of Robust Neural Networks
› 5.5 Assess the Impact of Neural Networks in Natural Disaster Management
› 5.6 Develop Proposals for Future Applications of Resilient Neural Networks
4 Foundations of Neural Network Robustness 5 chapters · 30 classes  85 marks
• 1 Understanding Neural Networks: Fundamentals and Architecture — 6 classes
› 1.1 Define Neural Networks: Key Concepts and Terminology
› 1.2 Explore Neural Network Architecture: Layers and Nodes
› 1.3 Analyze Activation Functions: Types and Their Impact
› 1.4 Examine Learning Algorithms: Training Neural Networks
› 1.5 Investigate Common Architectures: CNNs and RNNs
› 1.6 Apply Concepts: Designing a Simple Neural Network
• 2 Identifying Vulnerabilities: Common Weaknesses in Neural Networks — 6 classes
› 2.1 Analyze Common Weaknesses in Neural Networks
› 2.2 Explore Key Factors Affecting Neural Network Performance
› 2.3 Identify Sources of Vulnerability in Dataset Preparation
› 2.4 Investigate Architectural Flaws in Neural Network Design
› 2.5 Assess the Impact of Adversarial Attacks on Neural Networks
› 2.6 Develop Strategies to Mitigate Identified Vulnerabilities
• 3 Testing for Robustness: Methodologies and Tools — 6 classes
› 3.1 Identify Key Concepts in Neural Network Robustness
› 3.2 Explore Different Testing Methodologies for Robustness
› 3.3 Analyze Tools for Evaluating Neural Network Performance
› 3.4 Implement Unit Testing Strategies for Neural Networks
› 3.5 Conduct Stress Testing on Neural Network Models
› 3.6 Develop a Robustness Assessment Framework for Practical Application
• 4 Mitigation Strategies: Enhancing Neural Network Resilience — 6 classes
› 4.1 Identify Vulnerabilities in Neural Networks
› 4.2 Analyze Common Threats to Neural Network Integrity
› 4.3 Explore Data Augmentation Techniques for Robustness
› 4.4 Implement Adversarial Training to Enhance Resilience
› 4.5 Evaluate the Effectiveness of Regularization Methods
› 4.6 Design a Comprehensive Robustness Assessment Plan
• 5 Assessment Compliance: Aligning with ISO 24029 Standards — 6 classes
› 5.1 Understand ISO 24029 Standards for Neural Network Assessment
› 5.2 Identify Key Components of Robustness in Neural Networks
› 5.3 Evaluate Current Assessment Methods Against ISO 24029
› 5.4 Develop Assessment Criteria for Neural Network Compliance
› 5.5 Implement Best Practices for Aligning Neural Networks with ISO Standards
› 5.6 Analyze Case Studies of Neural Network Compliance Assessments
5 ISO Standards and Compliance 5 chapters · 30 classes  65 marks
• 1 Understanding ISO Standards in Artificial Intelligence — 6 classes
› 1.1 Define Key ISO Standards in Artificial Intelligence
› 1.2 Explore the Importance of Compliance in AI Systems
› 1.3 Identify the Components of ISO 24029 Certification
› 1.4 Assess the Role of Neural Networks in Achieving Standards
› 1.5 Examine Case Studies on ISO Compliance in AI
› 1.6 Develop an Action Plan for Implementing ISO Standards
• 2 Overview of ISO 24029 and Its Objectives — 6 classes
› 2.1 Define ISO 24029 and Its Key Components
› 2.2 Explore the Objectives of ISO 24029
› 2.3 Identify Stakeholders in the Assessment Process
› 2.4 Assess the Importance of Neural Network Robustness
› 2.5 Analyze Compliance Requirements under ISO 24029
› 2.6 Develop an Action Plan for Implementing ISO 24029
• 3 Framework for Assessing Neural Network Robustness — 6 classes
› 3.1 Define Neural Network Robustness and Its Importance
› 3.2 Explore ISO 24029 Standards for Neural Networks
› 3.3 Identify Key Criteria for Assessing Robustness
› 3.4 Analyze Common Assessment Methods for Neural Networks
› 3.5 Evaluate Real-World Case Studies of Neural Network Failures
› 3.6 Develop a Robustness Assessment Plan for a Neural Network
• 4 Compliance Requirements and Implementation Strategies — 6 classes
› 4.1 Overview Compliance Requirements for ISO 24029
› 4.2 Identify Key Stakeholders in Compliance Implementation
› 4.3 Analyze Current Practices Against ISO 24029 Standards
› 4.4 Develop Effective Compliance Strategies for Neural Networks
› 4.5 Create an Action Plan for Implementing Compliance Procedures
› 4.6 Evaluate and Monitor Compliance Effectiveness Post-Implementation
• 5 Case Studies and Future Trends in ISO Compliance — 6 classes
› 5.1 Analyze Key Case Studies in ISO 24029 Compliance
› 5.2 Identify Common Challenges in Implementing ISO Standards
› 5.3 Evaluate Trends in Neural Network Robustness Assessments
› 5.4 Discuss Future Directions for ISO Compliance in AI
› 5.5 Develop Strategies for Enhancing ISO Certification Processes
› 5.6 Create a Compliance Action Plan Based on Case Study Insights
6 Leadership in AI Projects 0 chapters  45 marks
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Curriculum current at the date of issue.