This course offers an intensive exploration of AI and Machine Learning (ML), focusing on their transformative roles across
various domains. Building on an introduction to AI and ML principles, the course delves into advanced applications, particularly
in computer vision, showcasing how these technologies are revolutionizing fields such as healthcare, autonomous vehicles,
and security systems. The rest of the curriculum is dedicated to addressing the critical safety and ethical challenges posed
by AI deployment. Through discussions and case studies students will examine the potential risks, biases, and ethical dilemmas
inherent in AI systems, learning strategies to mitigate these issues and ensure the responsible use of AI technology.
In this course, students are divided into groups to:
(1) As home work: Read the papers, make presentations
(2) at the class: present and discuss
In this course, students are divided into groups to:
(1) As home work: Read the papers, make presentations
(2) at the class: present and discuss
The purpose of class is to provide students with a well-rounded understanding of both the opportunities and challenges presented
by AI technologies. Additionally, this class aim to equip students paper-reading skills, presentation skills, etc.
- The students can understand the basics of AI and Machine Learning
- The students can understand the wide range applications of AI in Computer Vision applications
- The students can build up critical thinking about the ethical considerations and safety issues surrounding the deployment of artificial intelligence systems
| Presentation | Discussion | Total. | |
|---|---|---|---|
| 1. | 20% | 20% | 40% |
| 2. | 15% | 15% | 30% |
| 3. | 15% | 15% | 30% |
| Total. | 50% | 50% | - |
| Class schedule | HW assignments (Including preparation and review of the class.) | Amount of Time Required | |
|---|---|---|---|
| 1． | Introduction to Machine Learning | Investigate the basic concept of ML including neural networks, gradient descent, forward and backward propagation. | 190minutes |
| 2． | Basic models and their applications | Investigate the basic models including CNN, LSTM and their roles in practical applications | 190minutes |
| 3． | Advance models and their applications | Investigate the recent advanced models: transformer Everything LLMs: applications, variations, what can go wrong, how do we know and what to do about it ? Discussion with a guest: Blaine Rogers - Noeon Research, AI Safety Tokyo |
190minutes |
| 4． | The Alignment problem - an overview | Investigate the basic concepts of Artificial General Intelligence (AGI) and the AI Alignment problem - Outer & inner alignment problems: reward misspecification & instrumental convergence, deception, mesa-optimizer, goal misgeneralization etc. - Technical alignment: RLHF, Constitutional AI, debate, etc. |
190minutes |
| 5． | The principles of AI Alignment I | Build up the fundamental understanding regarding AI safety issues. Corrigibility? Discussion with a guest: Domenic Denicola - Google |
190minutes |
| 6． | The principles of AI Alignment II | How to investigate issues of AI safety and tackle the alignment problem Discussion with a guest: Esben Kran - Apart Research |
190minutes |
| 7． | Presentation and summary of each student | Summarize what you have learnt, how you can use the acquired knowledge for your future, and so on. | 190minutes |
| Total. | - | - | 1330minutes |
Presentation and communicative ability (50%) and discussion skill (50%) are the criteria of the grade. More than 60% of the
total score is needed for getting the course credit.
<Note>
Students are marked absent from the class if they are late regardless of the delay time.
If students are absent from more than one third of the total number of classes, the credit of this course cannot be given to them.
Even though students are absent from the class whatever the reason, e.g. sickness, delay of public transportation systems, forgetting to bring the student ID card, it is counted as absence.
<Note>
Students are marked absent from the class if they are late regardless of the delay time.
If students are absent from more than one third of the total number of classes, the credit of this course cannot be given to them.
Even though students are absent from the class whatever the reason, e.g. sickness, delay of public transportation systems, forgetting to bring the student ID card, it is counted as absence.
| ways of feedback | specific contents about "Other" |
|---|---|
| Feedback in the class |
Machine Learning Specialization (Coursera) - Stanford University
https://www.coursera.org/specializations/machine-learning-introduction#courses
https://www.coursera.org/specializations/machine-learning-introduction#courses
| Work experience | Work experience and relevance to the course content if applicable |
|---|---|
| N/A | N/A |
Last modified : Fri Sep 20 04:03:48 JST 2024