Curiasity | Responsible Safe AI Systems course

Course / Course Details

Responsible Safe AI Systems course

Artificial Intelligence

By - Aarthi M
0 students
N/A
(0)

Course Requirements

Course Description

Responsible Safe AI Systems is a foundational course designed to introduce learners to the principles, practices, and technologies required to develop ethical, transparent, secure, and trustworthy Artificial Intelligence systems. The course explores key concepts such as AI ethics, fairness, accountability, privacy, bias detection, explainable AI, AI governance, safety frameworks, and responsible deployment of AI applications. Learners will understand how AI impacts society and industries while gaining practical knowledge on building AI systems that are reliable, human-centered, and aligned with legal and ethical standards. The course also covers emerging regulations, risk management, and best practices for safe AI implementation in real-world scenarios.

Course Outcomes

Understand the fundamentals of Responsible AI and its importance in modern technology and society.
Identify ethical challenges and risks associated with AI systems, including bias, privacy, misinformation, and security concerns.
Apply fairness, accountability, and transparency principles in AI model development and deployment.
Analyze AI safety frameworks and governance policies used in organizations and industries.
Implement privacy-preserving and secure AI practices to protect data and users.
Evaluate AI models for bias, reliability, and explainability using appropriate techniques and tools.
Understand legal and regulatory requirements related to AI systems and responsible innovation.
Develop trustworthy AI solutions that are safe, inclusive, and beneficial for users and society.
Demonstrate awareness of human-centered AI design and the societal impact of intelligent systems.
Prepare for careers and research opportunities in ethical AI, AI governance, and safe AI system development.

Course Curriculum

1 chapters
45 lectures
0 quizzes
N/A total length

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1 Introduction Responsible Safe AI Systems IIITH IITM

7.07 Min

2 Introduction

26.39 Min

3 AI Capabilities Part 1

35.23 Min

4 AI Capabilities Part 2

37.06 Min

5 AI Risk Part 1

25.23 Min

6 AI Risk Part 2

39.32 Min

7 AI Risk Part 3

38.5 Min

8 AI Risk Part 4

28.36 Min

9 Robustness Part 1

43.05 Min

10 Robustness Part 2

34.18 Min

11 Robustness Hands On

38.31 Min

12 RLHF

34.48 Min

13 AI Alignment

26.2 Min

14 Transformers Part 1

41.53 Min

15 Transformers Part 2

23.13 Min

16 Hugging face

29.51 Min

17 Unlearning

30.49 Min

18 Approximate unlearning

30.59 Min

19 Evaluation of Unlearning and Graph Unlearning Part 1

35.48 Min

20 Evaluation of Unlearning and Graph Unlearning Part 2

20.47 Min

21 Representation Engineering Hands on

40.56 Min

22 Introduction to ML Part 1

35.19 Min

23 Introduction to ML Part 2

22.56 Min

24 Basics of Neural Networks PyTorch Part 1

28.31 Min

25 PyTorch Basic Workflow

26.44 Min

26 PyTorch Classification

26.16 Min

27 Basics of Neural Networks PyTorch Part 2

30.44 Min

28 Bias I

28.55 Min

29 Bias II

24.47 Min

30 Source of Bias

6.04 Min

31 Bias Handson

38.43 Min

32 Bias III

37.05 Min

33 Bias IV

45.41 Min

34 Bias Handon Part 1

9.44 Min

35 Bias Handson Part 2

12.33 Min

36 Data Privacy

1 Hour 11.33 Min

37 Differential Privacy

54.36 Min

38 Approximate Differential Privacy

35.48 Min

39 Exponential Mechanism

24.28 Min

40 Fairness in Machine Learning

1 Hour 4.26 Min

41 Interpretability I

51.03 Min

42 Interpretability II

34.48 Min

43 Interpretability Hands on Part 1

16.26 Min

44 Interpretability Hands on Part 2

28.11 Min

45 AI Policies Regulations AGI Part 1

49.47 Min

Instructor

Aarthi M

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Duration N/A
Lectures 45 lessons
Enrolled 0 students
Category: Artificial Intelligence
0 Quizzes
Language: English
Skill Level Beginner
Certificate of Completion
Full lifetime access

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