Data Science Course

Data Science and Beyond
Data science has become very important in recent years as a methodology
for inductively constructing models and hypotheses from data.
This trend has also spilled over into other academic disciplines, such as
medicine, education, disaster prevention, agriculture, economics, andlinguistics, leading to a surge in demand for data scientists in industries
spanning agriculture and manufacturing, distribution and retailing, andfinance and entertainment. The new Data Science Course, established inApril 2023 as a center for education and research in mathematics, data
science, and machine learning, also serves to foster top-class data science
experts.
By actively welcoming students and researchers from adjacent disciplines,
we also contribute to the creation of new interdisciplinary academic fields.

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Data Science Course

Data Science is Drawing a Lot of Attention
Thanks to advances in information and communication technologies, a wide
variety of data are now being collected and stored on an unprecedented scale. A lotof efforts are also being made in trying to inductively draw out knowledge fromsuch data. Various new developments are also emerging in statistical and machinelearning theories, which serve as frameworks to support the process of inductive
inference from data. Such advances are making it possible to systematically deal
with types of data that were previously difficult to handle, as well as to perform
new types of data analysis. Then there is deep learning. Starting with images andtext, varieties of data that are being accumulated in massive quantities, models
with superior capabilities that can be mistaken as the artificial “intelligence” havenow been constructed for different tasks, by appropriately training multilayer
neural networks. These striking achievements in deep learning have in turn beenraising new questions about frameworks for making inductive inferences from
data. Why does deep learning work so well? According to optimization theory, it
should be difficult to optimize the kind of non-convex objective functions thattypically appear in deep learning. Statistical learning theory also states thatcomplex models, such as multilayer neural networks, cannot be expected to have agood generalization ability. In the light of these findings, it is hard to explain why
deep learning works so effectively. In the search for an answer to this conundrum,
new systems and methodologies of deep learning, and by extension, data science,
are likely to emerge.
We invite you to dive with us into this exciting world of data science.
Data Science Course

Toshiyuki Tanaka

Data Science Course

March 1993: Received a doctoral degree from the University of Tokyo. April
1993: Assistant, Department of Electronics Engineering, Faculty of Technology,
Tokyo Metropolitan University. April 2000: Lecturer, Department of ElectricalEngineering, Graduate School of Engineering, Tokyo Metropolitan University.
January 2002: Associate Professor, Department of Electrical Engineering,
Graduate School of Engineering, Tokyo Metropolitan University. April 2005:
Associate Professor, Department of System Design, Faculty of System Design,
Tokyo Metropolitan University. October 2005: Professor, Department ofSystems Science, Graduate School of Informatics, Kyoto University.

Graduate School of Informatics

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Data Science CourseOutline
概 要■Group and Teaching Staff
Group

Teaching Staff

Statistical Mathematics

Hidetoshi Shimodaira/Professor Junya Honda/Associate Professor

Statistical Inference

Hisayuki Hara/Professor (Secondary appointment : Institute of Liberal Arts and Sciences)

InformationTheoretic Learning

Toshiyuki Tanaka/Professor Tomoyuki Obuchi/Associate Professor

Signal and Information
Processing

Kazunori Hayashi/Professor (Secondary appointment : Institute of Liberal Arts and Sciences)

Applied Machine Learning(Computational Intelligence)

Akihiro Yamamoto/Professor Ichigaku Takigawa/Program-Specific Professor
Brendan John Flanagan /Program-Specific Associate Professor(Institute of Liberal Arts and Sciences)

Applied Machine Learning(Collective Intelligence)

Hisashi Kashima/Professor Koh Takeuchi/Assistant Professor

Medical and Healthcare
Data Science

Hiroshi Tamura/Professor (Secondary appointment : Institute of Liberal Arts and Sciences)

Computational Intelligence
Systems (Adjunct unit)

Toshiyuki Tanaka/Professor Nonori Ueda/Adjunct Professor
Tomoharu Iwata/Adjunct Teaching Staff (NTT Communication Science Laboratories)■Curriculum of Data Science Course
CurriculumofDataScienceCourseDoctoralProgram(Informatics)

DoctoralThesis

rd3 
nd2 
st1 Subjects provided by the Course (total 6 credits)
SeminaronDataScienceAdv.E SeminaronStatisticalModeling,AdvancedA,BE� � SeminaronMachineLearning,AdvancedA,BE�
SeminaronAppliedDataScience,AdvancedA,BE� �(2creditseach)

Master’sProgram(Informatics)



Master’sThesis
nd2 

Seminarsandexercises
forMaster’sthesis

SubjectsprovidedbytheCourse(includingrecommendedcoursesprovidedbyotherCourses)

AdvancedSubjects

- 10 -

SeminarinDataScience(Master’sprogram,Mandatory2credits)
StatisticalSignalProcessing,ComputationalLearningTheoryE,StatisticalLearningTheoryE,
InformationͲtheoreticSystemsTheory,StatisticalSystemsTheory(2creditseach)



RecommendedSubjects
providedbyOtherCourses
ComputationalIntelligence(SS)

Other28subjects

BasicSubjectsst1 InterdisciplinarysubjectsofthePerspectivesinInformatics
(Mandatory2creditsormore,upto4credits)
PerspectivesinInformatics1 PerspectivesinInformatics2�
PerspectivesinInformatics3E PerspectivesinInformatics4E�
PerspectivesinInformatics5E (2creditseach)

Priorto
admission

Differential and Integral
Calculus

Linear algebra(Mandatory10credits)

AdvancedStudyinData
Science2E
(AssignedtoM2,5credits)
AdvancedStudyinData
Science1E
(AssignedtoM1,5credits)

FoundationofStatisticalScience(2credits)
DigitalTransformation,SecondaryUsageofData(1crediteach)

GeneralSubjectsprovidedbytheschoolPerspectivesinPlatformStudies(2credits),ComputationalScience,Introduction(2credits),ComputationalScience,ExerciseA(1credit),
InformationandIntellectualProperty(2credits),InnovationandInformation
(2credits),InformationAnalysisandManagement(2credit),Information
AnalysisandManagement,Exercise(1credit),SocialContributionsthrough
InformaticsE(1credit),InternshipintheFieldofInformaticsE(1credit)

Probability and statistics- 10 -59

ResearchGuidance

Specificsubjects
providedbythe
schoolFundamentalbackgroundofthesubjectsprovidedbythe
Course,etc.
Note:�Subjects�marked�with�the�letter�“E”�will�be�provided�in�English.

Data Science Course

Statistical Mathematics
Theory and Application of Statistics and Machine Learning
Statistics plays a vital role as the theoreticalfoundation of the highly influential trends of big
data, data mining, and AI. A key feature of statistics
is that it provides a methodology for making
inductive inferences from data, in a way that
accounts for randomness. On this foundation,
machine learning has developed very rapidly inrecent years, from extracting information fromlarge volumes of data to making decisions utilizing
only small amounts of data. Our aim at this turning
point is to develop new methodologies driven bymathematics and programming to use withreal-world data.
[Hidetoshi Shimodaira , Junya Honda]

Graph embedding using neural nets toreduce the dimensionality of data andperform image-text mutual search.

Statistical Inference
Mathematics of Statistical Inference in the Age of Big Data and Its Applications
using even small sample sizes. Our research andeducation are focused on the development ofhigh-dimensional inference methods and theirapplication to fields as diverse as archaeology,
anthropology, and cognitive science, by making use
not only of classical inference methods but also
various mathematical techniques such ascombinatorics, algebraic geometry, andoptimization methods.
[Hisayuki Hara]

Data Science Course

With the rise of big data, machine learning, andAI, statistical inference theory and algorithms forstatistical inference in high-dimensional complex
systems are increasingly important. Althoughmassive quantities of data are now readily
accessible, the complexity of the phenomena wewant to understand clearly remains daunting. It istherefore necessary to develop robust inference
methods that allow us to make reliable inferences

Many statistical models can be interpreted as algebraic varieties.
Algebraic quantities can offer valuable insights for statistical inference.

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Data Science CourseOutline

Information-Theoretic Learning
Information Mathematics for Machine Learning
Over recent years, deep learning has advanced

understood. Our aim is to shed light on theenormously, with a rich variety of ideas and models

mechanisms of these methods via utilizing

generated and proposed. In addition to deep

knowledge from information theory and thelearning, other methods of machine learning havestatistical mechanics of disordered systems.

also been developed for dealing with various[Toshiyuki Tanaka, Tomoyuki Obuchi]

challenges confronted in data science. In somecases, the reasons why such methods are effectiveor the properties of such methods are not even

Signal and Information Processing
Stochastic and Statistical Approaches for Understanding Mathematical Systems
Our focus in this field of signal information processing
is on the construction and the analysis of mathematicalmodels for stochastic and statistical problems thatemerge in various kinds of systems, and on thedevelopment of effective algorithms for practicalapplication of such models. More specifically, we aremaking use of statistical signal processing, an approachto systematizing methodologies for extracting useful
information from observed raw signals and data, as a tool
for tackling problems mainly in communications systems,s u c h a s m o b i l e c o m m u n i c a t i o n s , o p t i c a l fi b e r
communications, and IoT.
[Kazunori Hayashi]

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Example of super-resolution image by compressedsensing for X-ray fluorescence measurement

Data Science Course

Applied Machine Learning (Computational Intelligence)
Machine Learning and Data Science for Solving Real-world Problems
In our work on applied machine learning

making concrete implementation through scientific(computational intelligence), we conduct practical

discovery and understanding; through integration withresearch on machine learning and data science with ageneral discrete structure processing using symbolic

view to developing solutions to real-world problems

logic, knowledge representation, and algorithm design;and challenges in the fields of natural sciences andand by comparison between human and machine

education. In life sciences and chemistry, discretelearning.

combinatorial structures, such as genome sequences,[Akihiro Yamamoto, Ichigaku Takigawa,Brendan John Flanagan]molecular structures, andintermolecular interactions,n e e d t o b e r e fl e c t e d i nmachine learning. We aredeveloping technology toaddress these need. In
education, we are working on
the development of an
educational information
platform and methodology toimprove the effectiveness andefficiency of our own learning.
Our goal is to pave the way fora new foundation forcomputational intelligence, byData Science Course

Applied Machine Learning (Collective Intelligence)
Pioneering Innovative Applications of Data Analytics
We focus on research and development
in statistical machine learning and data
mining techniques, and design data-driven
solutions that address critical challenges
in various domains, such as healthcare,biotechnology, pharmaceuticals,education, transportation, and marketing.
By collaborating with industry leaders,
governmental agencies, and otherorganizations, our goal is to break new
ground in the applications of data analytics
and generate impactful solutions forreal-world problems.
[Hisashi Kashima , Koh Takeuchi]

Leveraging advanced analytics to address challenges across various domains.

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Data Science Course

OutlineMedical and Healthcare Data Science

The medical research outlined below is conducted mainly in Kyoto University Hospital.

Research Themes■ Epidemiological research utilizing clinical data including imaging and genetic data
Clinical research consisting mainly of observational studies, in combination with multimodal
imaging and genetic data, elucidating pathophysiology or predicting prognosis.

■ Epidemiological research utilizing claims data, including the National Database ofHealth Insurance Claims and Specific Health Checkups of Japan (NDB)
Epidemiological studies based on the secondary use of claims data, which plays a central role in
the Data Health Plan featured in the Japan Revitalization Strategy■ Research on implementation and improvement of hospital information systems for
improving hospital management
Research aimed at improving hospital management by organizing and verifying implementationof issues in the renovation, improvement, and secondary use of hospital information systems
involving new technologies such as IoT and AI■ Research on the development of a gaze
analyzing visual function evaluation device
Study on using a “gaze analyzing visual function
evaluation device” featuring eye tracking and AI foranalysis, to assess multiple visual functions, such as
visual acuity and eye movements, with a focus on thev i s u a l fi e l d . A f u r t h e r g o a l i s t o d e v e l o p m o r eadvanced eye tracking technologies suitable forscreening of cognitive impairment.

63[Hiroshi Tamura]Data Science Course

Computational Intelligence Systems (Adjunct Unit)

Toward Knowledge Creation from Diverse Data
With advances in deep learning, machine learning methods have achieved a high level of performancein image and language processing. However, when a large quantity of good training data is not available,
low performance becomes a problem. To enable the effective use of machine learning in a broader rangeof fields, we are working on machine learning methods that can achieve high performance even under
undesirable conditions, e.g., when there are only a small amount of data available or when high-quality
data cannot be obtained. More specifically, we are working on meta-learning, i.e., the process of learning
how to learn, and on machine learning that takes advantage of inductive bias.
[NTT Communication Science Laboratories: Naonori Ueda, Tomoharu Iwata]

Data Science Course

Graduate School of Informatics

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