2022 Faculty Courses School of Computing Major courses
Applied Artificial Intelligence and Data Science C 2
- Academic unit or major
- Major courses
- Instructor(s)
- Yoshihiro Miyake / Asako Kanezaki / Katsumi Nitta / Hiroshi Nagahashi / Takao Kobayashi / Takayuki Takigawa / Lupton Scott / Zhuang Yulin / Paulo Felnando / Zhumaity Viktor / Kobashikawa Carlos / Johansson Robert / Yoko Tanaka / Yong Lu
- Class Format
- Lecture
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 7-8 Wed
- Class
- 2
- Course Code
- XCO.T485
- Number of credits
- 100
- Course offered
- 2022
- Offered quarter
- 1Q
- Syllabus updated
- Jul 10, 2025
- Language
- English
Syllabus
Course overview and goals
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.
Course description and aims
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.
Student learning outcomes
実務経験と講義内容との関連 (又は実践的教育内容)
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.
Keywords
artificial intelligence, data science, machine learning, investment strategies, e-commerce, reinforcement learning, clinical development
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
This course requires students to take an active role in their own learning. It is required to attend each class.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| 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. |
Study advice (preparation and review)
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.
Textbook(s)
None required
Reference books, course materials, etc.
Materials will be provided on T2SCHOLA in advance and shared in Zoom lecture
Evaluation methods and criteria
Based on quizzes evaluating students' understanding at the end of each class.
Related courses
- XCO.T487 ： Fundamentals of data science
- XCO.T488 ： Exercises in fundamentals of data science
- XCO.T489 ： Fundamentals of artificial intelligence
- XCO.T490 ： Exercises in fundamentals of artificial intelligence
- XCO.T483 ： Advanced Artificial Intelligence and Data Science A
- XCO.T486 ： Advanced Artificial Intelligence and Data Science D
Prerequisites
Students of the doctor course are required to register XCO.T689-2 "Advanced Artificial Intelligence and Data ScienceC-2."
Contact information (e-mail and phone) Notice : Please replace from ”[at]” to ”@”(half-width character).
Katsumi Nitta nitta.k.aa[at]m.titech.ac.jp
Asako kanezakii kanezaki[at]c.titech.ac.jp
Office hours
Contact by e-mail in advance to schedule an appointment.