2021 Advanced Topics in Artificial Intelligence S

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Academic unit or major
Graduate major in Artificial Intelligence
Instructor(s)
Suzumura Toyotaro  Machida Motoya 
Course component(s)
Lecture     
Day/Period(Room No.)
Intensive ()  
Group
-
Course number
ART.T454
Credits
2
Academic year
2021
Offered quarter
1-2Q
Syllabus updated
2021/5/17
Lecture notes updated
2021/5/17
Language used
English
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Course description and aims

In this intensive course, advanced topics in the wide range of informatics such as mathematical information sciences, intelligence sciences, life-sciences and socio-economic sciences are introduced by visiting lecturers.
The aim of this course is to broaden students' perspectives by lectures of advanced topics by active scientists in the front line.

Student learning outcomes

Students can obtain knowledge about advanced topics in mathematical information sciences, intelligence sciences, life sciences and socio-economic sciences.

Keywords

mathematical information sciences, intelligence sciences, life sciences, socio-economic sciences

Competencies that will be developed

Specialist skills Intercultural skills Communication skills Critical thinking skills Practical and/or problem-solving skills

Class flow

Lectures give intensive lectures about selected advanced topics.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Advanced topics on graph algorithms Graph theory
Class 2 Advanced topics on graph database Graph theory
Class 3 Advanced topics on graph learning Machine learning
Class 4 Advanced topics on graph neural network (I) Neural network
Class 5 Advanced topics on graph neural network (II) Neural network
Class 6 Advanced topics on high performance computing and graph learning for massive graphs High performance computing
Class 7 Advanced topics on graph learning and use cases Graph theory
Class 8 Motivation for Monte Carlo simulation / Rejection algorithm Study of advanced topics
Class 9 Markov chain Monte Carlo method / Metropolis algorithm Study of advanced topics
Class 10 Discrete structure and Gibbs sampler / Gibbs algorithm Study of advanced topics
Class 11 How long should you run it? / Perfect sampling algorithms Study of advanced topics
Class 12 Hidden Markov model and dynamic decision making / Viterbi algorithm Study of advanced topics
Class 13 Quantum computation and sampling / Shor’s algorithm Study of advanced topics
Class 14 Brownian motion and intertwining dual / Pitman-type algorithm Study of advanced topics

Textbook(s)

None

Reference books, course materials, etc.

Specified by lecturers

Assessment criteria and methods

Will be based on exercise and report.

Related courses

  • None

Prerequisites (i.e., required knowledge, skills, courses, etc.)

None

Contact information (e-mail and phone)    Notice : Please replace from "[at]" to "@"(half-width character).

Toshiaki MUROFUSHI (murofusi[at]c.titech.ac.jp)

Other

The details will be announced later.

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