CSC316 — MACHINE LEARNING

About this course

CSC316 MACHINE LEARNING is a 300 Level course in the Computer Science department, taught in the Second Semester. On La-Net Hub you can watch all 30 recorded lectures for free, grouped into 10 series, read the course lecture note online or download it as a PDF, and test yourself with practice questions.

Ethics, security, and case studies in machine learning (ML) collectively address the responsible development and deployment of intelligent systems in real-world contexts. Ethical considerations focus on ensuring fairness, transparency, accountability, and the prevention of bias or discrimination in ML models, especially when they impact sensitive areas like healthcare, finance, or criminal justice. Security in ML involves protecting models and data from threats such as adversarial attacks, data poisoning, and privacy breaches, ensuring that systems remain robust and trustworthy. Case studies provide practical insights into how ML applications perform in real settings, highlighting both successful implementations and failures, thereby offering valuable lessons on ethical dilemmas, risk management, and best practices. Together, these aspects emphasize that beyond technical performance, ML systems must be developed with a strong commitment to societal well-being, safety, and trust.

Ethics, security, and case studies in machine learning (ML) are essential components for ensuring responsible and trustworthy AI systems. Ethics in ML focuses on fairness, accountability, transparency, and the avoidance of bias to prevent harm to individuals or groups, while also addressing issues such as data privacy and informed consent. Security in ML involves protecting models and data from threats like adversarial attacks, data poisoning, model theft, and privacy breaches, ensuring the integrity and reliability of ML systems in real-world applications. Case studies play a critical role by illustrating how ethical and security challenges emerge in practice, such as biased hiring algorithms, facial recognition misuse, or vulnerabilities in autonomous systems, providing valuable lessons and best practices for developers, researchers, and organizations. Together, these aspects help guide the development of robust, safe, and socially responsible machine learning solutions.

Lectures in this course

Series 1: Ethics, Security, and Case Studies in ML (Series 5)

1. Ethics, Security, and Case Studies in ML 1 11 minutes 34 seconds
2. Ethics, Security, and Case Studies in ML 2 10 minutes 37 seconds
3. Ethics, Security, and Case Studies in ML 3 10 minutes 49 seconds

Series 2: Ethics, Security, and case Studies in ML (SERIES 5)

1. Ethics, Security, and case Studies in ML 1 10 minutes 35 seconds
2. Ethics, Security, and case Studies in ML 2 9 minutes 47 seconds
3. Ethics, Security, and case Studies in ML 3 10 minutes

Series 3: Foundations of machine learning (series 1) · Part 1

1. Foundations of machine learning 1 9 minutes 14 seconds
2. Foundations of machine learning 2 8 minutes 41 seconds
3. Foundations of machine learning 3 9 minutes 14 seconds

Series 4: Foundations of machine learning (series 1) · Part 2

1. Foundations of machine learning 1 8 minutes 26 seconds
2. Foundations of machine learning 2 7 minutes 59 seconds
3. Foundations of machine learning 3 8 minutes 31 seconds

Series 5: Model Evaluation and performance metrics (series 3)

1. Model Evaluation and performance metrics 1 9 minutes 53 seconds
2. Model Evaluation and performance metrics 2 9 minutes 51 seconds
3. Model Evaluation and performance metrics 3 11 minutes 48 seconds

Series 6: model evaluation and performance metrics (series 3)

1. model evaluation and performance metrics 1 9 minutes 6 seconds
2. model evaluation and performance metrics 9 minutes 3 seconds
3. model evaluation and performance metrics 3 10 minutes 54 seconds

Series 7: supervised, Unsupervised, and Reinforcement learning (series 2)

1. supervised, Unsupervised, and Reinforcement learning 1 8 minutes 5 seconds
2. supervised, Unsupervised, and Reinforcement learning 2 9 minutes 16 seconds
3. supervised, Unsupervised, and Reinforcement learning 3 9 minutes 6 seconds

Series 8: Supervised, Unsupervised, and Reinforcement Learning (series 2)

1. Supervised, Unsupervised, and Reinforcement Learning 1 7 minutes 29 seconds
2. Supervised, Unsupervised, and Reinforcement Learning 2 8 minutes 33 seconds
3. Supervised, Unsupervised, and Reinforcement Learning 3 8 minutes 24 seconds

Series 9: Tools, Libraries, and practical Implementation (series 4)

1. Tools, Libraries, and practical Implementation 1 12 minutes 7 seconds
2. Tools, Libraries, and practical Implementation 2 11 minutes 34 seconds
3. Tools, Libraries, and practical Implementation 3 13 minutes 11 seconds

Series 10: Tools, Libraries, and Practical implementation (series 4)

1. Tools, Libraries, and Practical implementation 1 11 minutes 5 seconds
2. Tools, Libraries, and Practical implementation 2 10 minutes 39 seconds
3. Tools, Libraries, and Practical implementation 3 12 minutes 11 seconds

More Computer Science courses

• CSC 322 — Foundations of innovation and business concepts in computing
• CSC 322 A — COMPUTER SCIENCE INNOVATION AND NEW TECHNOLOGIES
• CSC310 — SYSTEM ANALYSIS AND DESIGN
• CSC312 — TECHNICAL WRITING
• CSC314 — SURVEY OF PROGRAMMING LANUAGES
• CSC318 — MODELING AND SIMULATION