Course title
Artificial Intelligence: Applications & Safety

Course content
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
Purpose of class
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.
Goals and objectives
  1. The students can understand the basics of AI and Machine Learning
  2. The students can understand the wide range applications of AI in Computer Vision applications
  3. The students can build up critical thinking about the ethical considerations and safety issues surrounding the deployment of artificial intelligence systems
Relationship between 'Goals and Objectives' and 'Course Outcomes'

Presentation Discussion Total.
1. 20% 20% 40%
2. 15% 15% 30%
3. 15% 15% 30%
Total. 50% 50% -
Class schedule

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 including NeRF, transformer and their roles in practical applications 190minutes
4. Artificial General Intelligence Investigate the basic concepts of Artificial General Intelligence (AGI), the capability requirements for an AI system to be defined as AGI, and evaluate if AGI is likely developed under current ML paradigm 190minutes
5. The principles of AI Alignment I Investigate how AI transform society over years. Build up the fundamental understanding regarding AI safety issues, for example reward misspecification and instrumental convergence 190minutes
6. The principles of AI Alignment II Investigate additional issues of AI safety and how to align it. 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
Evaluation method and criteria
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.

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.
Feedback on exams, assignments, etc.
ways of feedback specific contents about "Other"
Feedback in the class
Textbooks and reference materials
Machine Learning Specialization (Coursera) - Stanford University
Linear Algebra
Calculus 1 and 2
Office hours and How to contact professors for questions
  • Contact based on the appointments
Development of social and professional independence
    Active-learning course
    Course by professor with work experience
    Work experience Work experience and relevance to the course content if applicable
    N/A N/A
    Education related SDGs:the Sustainable Development Goals
    Last modified : Tue Mar 12 04:09:01 JST 2024