The goal of this course is to learn the frontiers of social implementation in artificial intelligence and data science.
The course is given by two classes (Class 1: given in Japanese, Class 2: given in English), and as shown in the lesson plan, overviews of the topic and recent trends are given by lecturers from companies.
This course aims to develop ability of each student to be more successful in the real world with the consideration of social implementation of artificial intelligence and data science.
✔ Applicable | How instructors' work experience benefits the course |
---|---|
Lectures of class 1 are given by scientists and engineers of Recruit Inc. and Nomura HD Inc., and lectures of class 2 are given by scientists and engineers of Nomura HD Inc. , Rakuten Group Inc. and Daiichi-Sankyo Inc., about application of AI and Data Science to solve practical problems. |
artificial intelligence, data science, machine learning, investment strategies, e-commerce, clinical development
✔ Specialist skills | Intercultural skills | Communication skills | Critical thinking skills | ✔ Practical and/or problem-solving skills |
This course requires students to take an active role in their own learning. It is required to attend each class.
Course schedule | Required learning | |
---|---|---|
Class 1 | AI and Data Science in Finance(1) | Understand the application of AI and data science in a Financial Company |
Class 2 | AI and Data Science in Finance(2) | Understand the application of AI and data science in a Financial Company |
Class 3 | Tips and Tricks for Building Large Scale Web Services | • Key Concepts About Web Scalability • Internet Business Trends • Common Terminology for Distributed Architectures • Dynamics of Growth • Scalable Design: High Traffic, Distributed Data • How to Prepare Organizations for Growth |
Class 4 | Data Science and UX/UI Designing | 1) Get ideas on how Data Science will be used in UX field; Web Analytics 2) Through real business cases, get to know how UX approach and Data Science will support each other 3) Through real business cases, get to know how important UI is for state-of-the-art technologies, as well as the other way around 4) With the new knowledge and idea above, widen and deepen your understanding of your own learning field; imagine potential issues in the social installation phase |
Class 5 | Lessons learned from AI innovation, from research to production. | - Show to students what it takes to move a complex AI project to production. - Share common challenges and the different ways AI projects can fail. - Share success stories with the students |
Class 6 | Apply data Analytics in Clinical Trial | Data analytics has been a recent trend to enhance Data Sciences capabilities in pharmaceutical industry. The concept, the industry needs, and the available technology supporting the industry application will be discussed. |
Class 7 | Opportunities and Challenges in the use of Real-World Evidence in the Clinical Development. | Learn that the real-world data (RWD) can be extensively used in the pharmaceutical industry for the product research and development. RWD sources, real-world evidence (RWE), advanced methods including propensity scores and AI/ML, and applications e.g., to inform clinical trial design and to serve as external control arms to support regulatory decision-making for single-arm clinical trials will be discussed. |
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.
None required
Materials will be provided on T2SCHOLA in advance and shared in Zoom lecture
No final exam will be given. The evaluation will be based on the reports of each assignment.
Students of the doctor course are required to register XCO.T689-2 "Advanced Artificial Intelligence and Data ScienceC-2."
Katsumi Nitta nitta.k.aa[at]m.titech.ac.jp
Asako kanezakii kanezaki[at]c.titech.ac.jp
Contact by e-mail in advance to schedule an appointment.