The goal of this course is to learn the forefront of social implementation in artificial intelligence and data science, and to consider the issues of social implementation of one's research.
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.
The purpose of this course is to deepen understanding of the social implementation of artificial intelligence and data science, and to enhance students' advanced abilities to play an active role in the real world.
✔ 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, reinforcement learning, 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 | Development of quantitative models as investment strategies | Introduce several quantitative models for investment from traditional frameworks to machine learning applications |
Class 2 | Data Science Lifecycle from Conception to Delivery: Trade Surveillance Case Study | Introduction to the data science project management lifecycle using a real-world industry example. |
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 | (1) Introduction to Data Science in Business (2) Reinforcement Learning for Business Control | (1) Introduction of data science and survey of applications in business with focus on e-commerce (2) Learning, Exploring and Acting to optimize Key Performance Indicators on the example of e-commerce search |
Class 5 | (1) Data Science solutions in e-commerce operations and fulfillment (2) Estimation of User's Interest for Product Recommendation | (1) Optimization applications: Price, Inventory, and Logistics (2) Method of Product Recommendation |
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 inform clinical trial design, provide a lacking context in the clinical trial to help optimize the informed regulatory decision-making in better understanding of the clinical trial data. |
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.
None
Materials will be provided on T2SCHOLA in advance and shared in Zoom lecture
Based on quizzes evaluating students' understanding at the end of each class and a term-end report.
This course is intended for doctoral students. For other students, please take Applied AI and Data Science C (XCO.T485-1, XCO.T485-2).
Katsumi Nitta nitta.k.aa[at]m.titech.ac.jp
Asako kanezaki kanezaki[at]c.titech.ac.jp
Contact by e-mail in advance to schedule an appointment.