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- Liberal arts and basic science courses
- Academic unit or major
- Graduate major in Artificial Intelligence
- Instructor(s)
- Sakuma Jun
- Class Format
- Lecture (HyFlex)
- Media-enhanced courses
- Day/Period(Room No.)
- Tue5-6(M-178(H1101)) Fri5-6(M-178(H1101))
- Group
- -
- Course number
- ART.T555
- Credits
- 2
- Academic year
- 2023
- Offered quarter
- 3Q
- Syllabus updated
- 2023/9/15
- Lecture notes updated
- -
- Language used
- English
- Access Index
-
Course description and aims
This course provides an advanced look at responsible artificial intelligence. We will understand machine learning in a unified way from the framework of empirical risk minimization and analyze the generalization capabilities of machine learning. Students will then learn the operating principles of deep learning, the requirements for decisions made by AI to be trusted, and the theory and technologies for achieving these requirements. Specifically, students will learn about explainable AI, data privacy protection, AI fairness, and AI security. The aims of this course are to 1) acquire knowledge of the principles and mechanisms of machine learning and deep learning and 2) understand the mechanisms necessary for machine learning and deep learning to be trusted by humans.
Student learning outcomes
By the end of this course, students will learn the following:
1) machine learning technologies
2) responsible AI technologies
Course taught by instructors with work experience
| ✔ Applicable | How instructors' work experience benefits the course |
|---|---|
| Experience working with data science/AI in a private company |
Keywords
Empirical risk minimization, statistical learning theory, deep learning, explainable AI, data privacy, AI fairness, AI security
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | Critical thinking skills | Practical and/or problem-solving skills |
Class flow
Every class consists of a lecture using the slides and the exercise
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Introduction to responsible AI | Lecture objectives and overview of respoisible AI |
| Class 2 | Empirical risk minimization 1 | Understanding the framework of supervised learning and regularization |
| Class 3 | Empirical risk minimization 2 | Understanding the framework of empirical risk minimization |
| Class 4 | Statistical Learning Theory 1 | Understanding the relationship between predictive loss and empirical Loss |
| Class 5 | Statistical Learning Theory 2 | Kernel theory |
| Class 6 | Deep Discriminative Models | How Neural Networks and CNNs worksへ |
| Class 7 | Deep Generative Models | How generativbe models and variational autoencoder works |
| Class 8 | Explainable AI | Understanding of methodologies of various explainable AI |
| Class 9 | Data Privacy 1: Differential privacy | Introduction to data privacy and privacy protection of statistical data by differential privacy |
| Class 10 | Data Privacy 2: Multi-party computation | Mechanisms of secure multiparty computation |
| Class 11 | AI Fairness | Understanding the causes of discrimination in AI decision-making and achieving fair AI |
| Class 12 | AI Security 1: Adversarial example | Attacks on AI at test time and its defense |
| Class 13 | AI Security 2: Poisoning | Attacks on AI at training time and its defense |
| Class 14 | AI Security 3: Security of Generative models | Security issues in generative/fundamental models |
Out-of-Class Study Time (Preparation and Review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Textbook(s)
Slides provided
Reference books, course materials, etc.
Foundations of Machine Learning, Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar, The MIT Press
Assessment criteria and methods
Evaluation by final report
Related courses
- CSC.T254 ： Machine Learning
- ART.T458 ： Advanced Machine Learning
- CSC.T242 ： Probability Theory and Statistics
- CSC.T272 ： Artificial Intelligence
- CSC.T352 ： Pattern Recognition
Prerequisites (i.e., required knowledge, skills, courses, etc.)
Some review will be given, but experience with elementary linear algebra, analysis, probability theory, and statistics at the undergraduate level is required.
