Educational Activities at Matsuo Laboratories

Matsuo Laboratory engages in practical educational activities centered on “Web Engineering and Business Models”, the Global Consumer Intelligence Endowed Chair (GCI), and Deep Learning JP. Through providing these classes, we aim to nurture human resources who will lead the next generation of the modern information society.

In the “Web Engineering and Business Models” class at Technological Management for Innovation (TMI), students learn engineering skills for the modern information society through the development of services using the Internet which is a fundamental technology for society.

“Global Consumer Intelligence Endowed Course” is a course to develop data scientists who can analyze data. The class has been offered since 2014, with a total of more than 200 students participating. 

“Deep Learning.JP” is the first educational program on Deep Learning provided in Japan, starting from November 2015. We provide hands-on education for students regardless of their faculties and year level.

Matsuo Lab’s AI Human Resource Development Method

  1. Experience-basedIn computer science, especially in the field of AI such as machine learning and deep learning, it is very difficult to understand just by listening to theoretical explanations. Important concepts and techniques can be acquired only through experience in coding, training models, and adjusting parameters. This is why almost all classes offered by Matsuo Laboratory are structured around programming
  2. CompetitionAs mentioned earlier, in areas such as machine learning and deep learning, students can gain a deep understanding of important concepts and techniques by working with real data and training models. Many classes incorporate practical Kaggle-style competitions in which students compete with each other for the accuracy of the models they have created. The goal is for students to learn important concepts and techniques to improve the model’s accuracy. Several classes also have a final presentation, where students are asked to engage in a project and present their results.
  3. Community-Based EducationMatsuo Laboratory proposes “community-based education” as a new approach to education. In the fields of Information and Communication Technologies (ICT) and Artificial Intelligence (AI)I, where technology is rapidly evolving, it is becoming increasingly difficult for faculty to provide all the latest topics together as a traditional lecture. In many classes, we value the formation of a student community and mutual mentoring through teamwork and projects.

Course list

Name of lecture Prior knowledge needed for this course Participants profile Goals upon completion
Programming Math
Data Scientist Training Course ☆ ☆☆ Students must know elementary statistics and linear algebra at the university level and have experience in programming. The student has acquired a set of skills related to data analysis, including statistical analysis, machine learning, and big data analysis, and is able to solve various kinds of real-world problems as a data scientist.
Fundamentals of Deep Learning Course ☆☆☆ ☆☆☆☆ Students must know linear algebra, calculus, probability, and statistics at an engineering college level, and have experience in numerical analysis in Python. The student understands the theoretical structures of Deep Learning and can create new models. The student is also capable of building and developing their own models based on Deep Learning related papers.
Deep Learning Applications Course – Generative Models ☆☆ ☆☆☆☆ Students must have programming experience in Python and knowledge of linear algebra, calculus, probability, and statistics, and should have completed a basic course in Deep Learning or have equivalent or equivalent knowledge. The student can use a wide range of basic algorithms and understand and implement state-of-the-art methods for generative modeling techniques in the field of Deep Learning.
Deep Learning Applications – Reinforcement Learning ☆☆ ☆☆ Students who have either completed the Basic Course in Deep Learning or have equivalent or higher knowledge. The student can use a wide range of basic algorithms and understand and implement state-of-the-art methods for reinforcement learning techniques in the field of Deep Learning.
Deep Learning Applications – NLP ☆☆ ☆☆☆ Students who have either completed the Basic Course in Deep Learning or have equivalent or higher knowledge The student can use a wide range of fundamental algorithms and understand and implement state-of-the-art methods for natural language processing techniques in the field of Deep Learning.
Deep Learning Practical Development Course (DL4US) ☆☆ ☆ Students who have experience in programming using Python and know elementary statistics and linear algebra at the university level. The student can apply key Deep Learning techniques such as image recognition, Natural Language Processing (NLP), generative modeling, and reinforcement learning to applications.
Web Engineering and Business Models ☆ ☆ Students who have programming experience. The student can work with web technologies and can implement key business strategies in the web field with a good understanding of the topic.

Roadmap for Learning Artificial Intelligence and Deep Learning

For those who have never studied artificial intelligence and deep learning, Matsuo Lab introduces a roadmap for learning.

Please refer to the Roadmap for Learning Artificial Intelligence and Deep Learning. (The roadmap is currently only available in Japanese)