Trang chủ

Development

Game Development

Artificial Intelligence (AI)

EXPLAINABLE AI

Loại khoá học: Game Development

AN EXPLORATORY JOURNEY TO DIMYSTIFY XAI

50.000 VND

1.699.000 VND

Đầy Đủ Bài Giảng

Học Online Tiện Lợi

Kích Hoạt Nhanh 2-5 Phút

Thanh toán tự động

Được phép tải xuống

Mô tả

Title: Demystifying AI: An Exploratory Journey into Explainable Artificial Intelligence

Outline:

I. Introduction to Explainable AI A. Defining Explainable AI B. Importance and motivations for Explainable AI C. Ethical and legal considerations

II. Fundamentals of Artificial Intelligence A. Overview of AI and its various branches B. Machine Learning algorithms and models C. Deep Learning and Neural Networks D. Explainability challenges in traditional AI approaches

III. Explainability in Machine Learning A. Black-box vs. White-box models B. Interpretable machine learning algorithms (e.g., decision trees, linear models) C. Post-hoc explainability techniques (e.g., feature importance, partial dependence plots) D. Trade-offs between model performance and interpretability

IV. Interpretable Deep Learning A. Challenges in interpretability of deep neural networks B. Layer-wise relevance propagation and saliency maps C. Activation maximization and feature visualization D. Network dissection and concept activation vectors E. Adversarial attacks and interpretability

V. Rule-based and Symbolic AI A. Rule-based expert systems B. Knowledge representation and reasoning C. Rule induction and decision rules D. Combining symbolic and sub-symbolic AI techniques

VI. Explainability in Natural Language Processing (NLP) A. Challenges in understanding NLP models B. Attention mechanisms and interpretability C. Explainable dialogue systems D. Interpretable sentiment analysis and text classification

VII. Evaluating and Assessing Explainable AI A. Metrics for evaluating explainability B. Human perception of explainability C. Assessing trade-offs between accuracy and interpretability D. Model-agnostic and model-specific evaluation methods

VIII. Applications and Case Studies A. Healthcare: Interpretable medical diagnosis systems B. Finance: Transparent credit scoring and fraud detection C. Law: Explainable legal decision support systems D. Autonomous vehicles: Explainable perception and decision-making E. Social implications and transparency in AI deployment

IX. Future Directions and Challenges A. Advances in Explainable AI research B. Regulatory and policy considerations C. Improving transparency and accountability in AI systems D. Human-AI collaboration and trust

X. Conclusion A. Recap of key concepts and insights B. Call to action for responsible AI development C. Final thoughts on the future of Explainable AI

Bạn sẽ học được gì

EXPLAINABLE AI

EXPLAINIBILITY IN MACHINE LEARNING

INTERPRETABLE DEEP LEARNING

RULE BASED AND SYMBOLIC AI

Evaluating and Assessing Explainable AI

Applications and Case Studies

Future Directions and Challenges

Yêu cầu

Basic Knowledge of Artificial Intelligence: Learners should have a foundational understanding of artificial intelligence concepts, including machine learning algorithms, neural networks, and their applications. Familiarity with AI terminology and principles will help in grasping the concepts discussed in the course.
Programming Skills: A basic understanding of programming is valuable for comprehending the implementation aspects of XAI techniques. Proficiency in a programming language commonly used in AI, such as Python, is recommended. Learners should be comfortable writing code, running scripts, and manipulating data.
Data Analysis Skills: Proficiency in data analysis techniques, including data preprocessing, feature engineering, and model evaluation, is crucial for applying XAI techniques effectively. Learners should be comfortable working with datasets, performing exploratory data analysis, and understanding data quality considerations.
Learning Mindset: XAI is a dynamic and evolving field, and learners should have a willingness to engage in continuous learning and keep up with the latest research and developments. Curiosity, critical thinking, and an open mind are important traits for gaining a deeper understanding of XAI concepts and their implications.
Time Commitment: Learners should allocate sufficient time for studying and completing the course materials. XAI can be a complex subject, and dedicating regular time for learning and practice will enhance comprehension and mastery of the concepts.

Nội dung khoá học

10 sections

Introduction

3 lectures

Introduction

03:37

Defining Explainable AI

03:24

Importance and motivations for Explainable AI

04:18

Fundamentals of Artificial Intelligence

5 lectures

Overview of AI and its various branches