convergence informatics - harvard university · brief introduction of professor, definition of the...

Convergence Informatics

Chapter 1 & 2 (Week 1) Course overview & introduction

DCCS326 Korea University 2019 Fall

Asst. Prof. Minseok Seo

[email protected]

01 Course Overview Convergence (Multidisciplinary) Informatics

Contents

Definition of Informatics

Introduction to Informatics 2.

Brief introduction of professor & course

Course Overview 1. Object & Aim of the course

Assignments & Quiz

Evaluation

Application fields of the Informatics

Core technology of Informatics

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Course Overview Course information

Convergence (Multidisciplinary) Informatics, DCCS326, Fall 2019.

Lecture time: Mon. (2,3) and Thu (9)

Location: 7-324

Completion division: Major elective subject

Level: Junior / Senior

Informatics is actually very difficult, but this course would be not

difficult !


Course Overview Brief introduction of professor, Definition of the “Convergence (Multidisciplinary)”

B.Sc. 2006.02 – 2010.02

Dept. of Computer Science

Ph.D. 2012.06 – 2016.02

Dept. of Statistics & Animal/Plant Science

Dept. of Microbiology & Population Genetics

Interdisciplinary Program in Bioinformatics

Seoul National University

M.Sc. 2010.02 – 2012.02

Dept. of Medical Science

PostDoc. 2017.04 – 2019.08

Dept. of Medicine

Harvard Medical School

Brigham & Women’s Hospital

Why did you study different fields?

Key question could be…

Convergence (Multidisciplinary) era


Course Overview Brief introduction of professor, Definition of the “Convergence (Multidisciplinary)”

Asst.Prof. 2019.09 – present

Dept. of Computer Convergence Software

Korea University

Married… & Have two children, Ryan & Logan

This is first class in this department

I’d like to convey at least a sense of convergence (Multidisciplinary)

field to you to help your next career


Course Overview Definition of Informatics


Course Overview Definition of Informatics

Etymology

‘Informatik’ (Steinbuch, K. 1957), German

‘Informatik’ == Computer Science (in English)

‘Informatique’ (Philippe, 1692), French

‘Informatics’ (Walter, 1967)

Every day we are touched/influenced by informatics

E-mail Google Youtube Facebook Kakao

Actually, Informatics is applied in most disciplines and industries.

Pharmacology Medicine Biology Smart

manufacturing

Network


Course Overview Object & Aim of the course

Students who have taken this course expect to be able to learn:

Convergence

Informatics

Concept of Data Science

Application of Informatics

Computational technologies

for Informatics

Statistical skill for Informatics

R programming

Bioinformatics &

Computational Biology


Course Overview Course schedule (Before Mid-term exam)

Week Period Study Contents

1 09.02 - 09.08 Introduction to Informatics applied in various fields I (Applications in Medicine, Pharmacology, Biology, Agricultural science, Smart farm and

etc.)

2 09.09 - 09.15 Introduction to Informatics applied in various fields II (Applications in Social science, Network science, Smart manufacturing, Meteorology,

Autonomous vehicles, and etc.)

3 09.16 - 09.22 R programming & Data analysis I (Installation, Data input/output, Data visualization, Fundamental grammar such as

conditional statement and loop, and etc.)

4 09.23 - 09.29 R programming & Data analysis II (Statistical estimation and prediction)

5 09.30 - 10.06 Clustering analysis (Unsupervised learning methods) (Illustrate examples of clustering analytics in various fields)

6 10.07 - 10.13 Algorithms of Dimensionality Reduction

7 10.14 - 10.20 Classification analysis (Supervised learning methods) (Illustrate examples of classification/prediction issue in various fields)

8 10.21 - 10.27 Mid-term Exam


Course Overview The second half of this course

It’s impossible to cover all fields because the applications of Informatics are

actually endless.

So, we will study at bio-medical science field, where the concept of

Informatics is best established in the second half of this course.


Course Overview Bioinformatics

R language will be used in this class.

It is not required any prior knowledge of the R language because I plan to provide

example code for student's practice.

It also does not require prior knowledge of biology and medicine.

The minimum knowledge of biology and medicine required to proceed course will

be learned in the theory class.

https://cran.r-project.org/

Monday 2hrs

Thu. Day 1hr

Lecture for Theory Hands-on lecture With R-code

Homework

Practice R-code with real data

Course flow to fully understand bioinformatics


Course Overview Course schedule (After Mid-term exam)

Week Period Study Contents

9 10.28 - 11.03 Introduction to Bioinformatics & Computational Biology (Accompanying knowledge of the basic level of Biology and Medicine)

10 11.04 - 11.10 Algorithm for DNA / RNA / Protein sequencing data analysis (This content technically belongs to the text mining category)

11 11.11 - 11.17 Statistics in Bioinformatics & other multidisciplinary fields

12 11.18 - 11.24

Discovery of significant biomarkers for personalized medicine or

specific biological phenomena (This content technically belongs to the feature selection/extraction category)

13 11.25 - 12.01 Gene expression profiling using clustering/network approach (This content technically belongs to the clustering category)

14 11.02 - 12.08 Machine learning approaches for disease/prognosis prediction (This content technically belongs to the classification category)

15 12.09 - 12.15 Role of software scientists for interdisciplinary work

16 12.16 - 12.22 Final Exam


Course Overview Mid-term and final exams

You can earn 100 points if you follow the course faithfully.

This means that I will not ask questions that are quite difficult in the exam.

About 70% of questions will be taken on the slides of theoretical lecture, and the

rest will be derived from hand-on assignment.


Course Overview Quiz & Assignment (Homework)

There will be two simple quizzes in class to check the student's learning

progress of the course.

Depending on the situation, it may be replaced by a homework instead of a

quiz. In that case, assignment will be 6 times and 30 percent of the total score.

Quiz

Homework

There will be 4-6 times assignments based on the R-programming.

It’s not difficult & just application of theoretical knowledge into real data.


Course Overview Textbook

No Textbook

This course will be proceed based on the presentation slide

I will upload presentation slide in Blackboard & my homepage

Homepage: https://scholar.harvard.edu/msseo

Teaching >> Multidisciplinary Informatics >> Related Materials

Reference 1

Introduction to Data Science by Rafael A. Irizarry, 2019.

(online textbook and free)

https://rafalab.github.io/dsbook/

Reference 2

R for Data Science by Garrett Grolemund.


https://r4ds.had.co.nz/

Reference 3

Modern Statistics for Biology by Wolfgang Huber.


https://www.huber.embl.de/msmb/index.html


Course Overview Evaluation plan

Absolute grading system

Score ≥ 95, you will get A+

Score ≥ 90, you will get A

Score ≥ 85, you will get B+

and...

30%

30%

10%

20%

10%

Midterm Final Quiz Assignment Attendance


Course Overview For successful class

Welcome questions related to any type of course.

One of the good academic habit is Googling.

Before asking anyone, do a Googling and ask questions that you still don't

understand.

Other knowledge gained from googling will accumulate and revisit.

In order to maximize learning outcomes, students should actively participate in

discussions.


Course Overview Contact information

Prof. Minseok Seo

Location: 7-203

Tel: TBA

Email: [email protected]

TA. Sungwon Moon

Location: 7-308

Email: [email protected]

If you have any questions about the course please email me and I will reply as

soon as I see it.

If you need to meet in person, please make an appointment by email first.

I will be available at Mon: 12:00 - 17:00 | Wed: 10:00 - 13:00 | Thu: 10:00 - 13:00.

End of Slide

convergence informatics - harvard university · brief introduction of professor, definition of the...

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