Generation of large amount of data has posed challenges in modeling biological and immunological processes. There is a clear need for mathematical and computational tools that are capable of analyzing large amount of biological information. Ordinary and partial differential equations, Monte Carlo simulations, agent-based models are emerging as powerful methods for studying biological problems. This course covers some basics of these mathematical and computational methods and their biomedical/bioengineering/biotechnology applications. We also discuss data analysis based on statistical approaches such as machine learning/AI. Such computational methods allow us to carry out important classification tasks in biological and biomedical sciences.
In lecture 1, you will be introduced to ordinary differential equations (ODEs) as applied in quantitative study of biological processes. We will emphasize study of biological kinetics and biological data analysis. Among applications, we will mention kinetic parameters in receptor-ligand binding and precision medicine. We will also briefly discuss dynamical systems analysis for ODEs.
In lecture 2, you will be introduced to partial differential equations (PDEs) as applied in quantitative study of biological processes. We will emphasize study of diffusion equation and biological data analysis. We will also discuss application problems such as selecting effective antibiotics (for bacterial infection in a given patient) utilizing disk diffusion methods.
In lecture 3, you will be introduced to kinetic Monte Carlo methods through random walk and directed walk simulations. Lectures will cover computer implementations of simulation algorithms and computer programs (C; random walk simulation in MATLAB and python). Some discussion on random numbers and parallel computation.
In lecture 4, we will discuss computational modeling of infectious diseases (e.g. hypermigration of immune cells in the context of COVID-19). We will also briefly mention about biological pathway modeling.
In lecture 5, you will be introduced to machine learning and artificial intelligence for solving biological/immunological problems. We will emphasize artificial neural network (ANN) based methods for artificial intelligence. Applications will be discussed such as vaccine epitope prediction/design utilizing various machine learning/AI based methods and software.
Generation of large amount of data has posed challenges in modeling biological and immunological processes. There is a clear need for mathematical and computational tools that are capable of analyzing large amount of biological information. Ordinary and partial differential equations, Monte Carlo simulations, agent-based models are emerging as powerful methods for studying biological problems. This course covers some basics of these mathematical and computational methods and their biomedical/bioengineering/biotechnology applications. We also discuss data analysis based on statistical approaches such as machine learning/AI. Such computational methods allow us to carry out important classification tasks in biological and biomedical sciences.
In lecture 1, you will be introduced to ordinary differential equations (ODEs) as applied in quantitative study of biological processes. We will emphasize study of biological kinetics and biological data analysis. Among applications, we will mention kinetic parameters in receptor-ligand binding and precision medicine. We will also briefly discuss dynamical systems analysis for ODEs.
In lecture 2, you will be introduced to partial differential equations (PDEs) as applied in quantitative study of biological processes. We will emphasize study of diffusion equation and biological data analysis. We will also discuss application problems such as selecting effective antibiotics (for bacterial infection in a given patient) utilizing disk diffusion methods.
In lecture 3, you will be introduced to kinetic Monte Carlo methods through random walk and directed walk simulations. Lectures will cover computer implementations of simulation algorithms and computer programs (C; random walk simulation in MATLAB and python). Some discussion on random numbers and parallel computation.
In lecture 4, we will discuss computational modeling of infectious diseases (e.g. hypermigration of immune cells in the context of COVID-19). We will also briefly mention about biological pathway modeling.
In lecture 5, you will be introduced to machine learning and artificial intelligence for solving biological/immunological problems. We will emphasize artificial neural network (ANN) based methods for artificial intelligence. Applications will be discussed such as vaccine epitope prediction/design utilizing various machine learning/AI based methods and software.
AI시대의 인간'을 정의하는 인문학자 구본권 !
오직 마이크임팩트에서만 전하는 <'인공지능'에 대한 Insight>
[마이크임팩트 GFC : Grand Future Class] - 미래수업
이상적인 예언이 아닌 이성적인 예측으로 !
포스트코로나, 인공지능, 투자예측, 트렌드 등
국내 최고의 미래학 전문가들이 전하는 인사이트 타임머신 !
미래는 볼 수 없기에.
과학은 말합니다. 미래로의 시간여행은 불가능하다고.
그렇다면 급변하는 시대, 불안한 미래를 우리는
어떻게 대처해야 할까요? 삶에도 망원경이 필요합니다.
넓고 멀리, 선명하고 정확하게 볼 수 있는 망원경.
국내 최고의 미래학자들이 들려주는 미래수업은
삶과 세상을 관찰하는 망원경이 될 것입니다.
당신의 미래에 대한
비관은 '진취적인 비판'으로,
낙관은 '근거있는 긍정'으로 바꿔줄 미래 성장 가이드 !
"시간은 지금 이 순간에도 절대적으로 흐른다. 우리는 지금이 아닌 미래를 사는 것이다."
<목차>
누구도 피해갈 수 없는 '정해진 미래'
불안한 '미래의 직업'
미래는 예측 가능한가
어떻게 해야 감춰진 걸 볼 수 있을까?
지식정보 사회의 핵심능력
질의 응답
AI시대의 인간'을 정의하는 인문학자 구본권 !
오직 마이크임팩트에서만 전하는 <'인공지능'에 대한 Insight>
[마이크임팩트 GFC : Grand Future Class] - 미래수업
이상적인 예언이 아닌 이성적인 예측으로 !
포스트코로나, 인공지능, 투자예측, 트렌드 등
국내 최고의 미래학 전문가들이 전하는 인사이트 타임머신 !
미래는 볼 수 없기에.
과학은 말합니다. 미래로의 시간여행은 불가능하다고.
그렇다면 급변하는 시대, 불안한 미래를 우리는
어떻게 대처해야 할까요? 삶에도 망원경이 필요합니다.
넓고 멀리, 선명하고 정확하게 볼 수 있는 망원경.
국내 최고의 미래학자들이 들려주는 미래수업은
삶과 세상을 관찰하는 망원경이 될 것입니다.
당신의 미래에 대한
비관은 '진취적인 비판'으로,
낙관은 '근거있는 긍정'으로 바꿔줄 미래 성장 가이드 !
"시간은 지금 이 순간에도 절대적으로 흐른다. 우리는 지금이 아닌 미래를 사는 것이다."
<목차>
누구도 피해갈 수 없는 '정해진 미래'
불안한 '미래의 직업'
미래는 예측 가능한가
어떻게 해야 감춰진 걸 볼 수 있을까?
지식정보 사회의 핵심능력
질의 응답
please search up Kidd math channel on youtube to find the new home of my courses.
A free course on the topic of Complex Number, designed according to the latest syllabus of IB Math AI HL, under topic 1: Algebra
Designed to the need of an HL student. It also included exam question demonstrations to show IB exam skills.
This course is meant to be quick but covers all the essentials of the topic Complex Number.
Disclaimer: If you are under 18 please ask a parent or guardian to open your account, handles any enrollments, and manages your account usage. As a rule of Udemy, your parents or guardians should be supervising your learning.
Content includes:
Definition of Complex Number
Use of complex Plane
Polar form and De Moivres's Theorem
Roots of Complex numbers
Trigonometric Identities from De Moivres's Theorem
Detail Content:
Complex Number
Complex plane
Polar Form
Euler Form
Sums, products and quotients in Cartesian, polar or Euler forms and their geometric interpretation
Complex conjugate roots
De Moivres's theorem
Powers and roots of complex numbers
Bonus content: Trigonometric Identities from De Moivres' Theorem
You are also welcomed to message me if you have any trouble.
Description from IB Syllabus:
AHL content
Recommended teaching hours: 20
The aim of the AHL content in the number and algebra topic is to extend and build upon the aims, concepts
and skills from the SL content. It introduces students to some important techniques for expansion,
simplification and solution of equations. Complex numbers are introduced and students will extend their
knowledge of formal proof to proof by mathematical induction, proof by contradiction and proof by
counterexample.
please search up Kidd math channel on youtube to find the new home of my courses.
A free course on the topic of Complex Number, designed according to the latest syllabus of IB Math AI HL, under topic 1: Algebra
Designed to the need of an HL student. It also included exam question demonstrations to show IB exam skills.
This course is meant to be quick but covers all the essentials of the topic Complex Number.
Disclaimer: If you are under 18 please ask a parent or guardian to open your account, handles any enrollments, and manages your account usage. As a rule of Udemy, your parents or guardians should be supervising your learning.
Content includes:
Definition of Complex Number
Use of complex Plane
Polar form and De Moivres's Theorem
Roots of Complex numbers
Trigonometric Identities from De Moivres's Theorem
Detail Content:
Complex Number
Complex plane
Polar Form
Euler Form
Sums, products and quotients in Cartesian, polar or Euler forms and their geometric interpretation
Complex conjugate roots
De Moivres's theorem
Powers and roots of complex numbers
Bonus content: Trigonometric Identities from De Moivres' Theorem
You are also welcomed to message me if you have any trouble.
Description from IB Syllabus:
AHL content
Recommended teaching hours: 20
The aim of the AHL content in the number and algebra topic is to extend and build upon the aims, concepts
and skills from the SL content. It introduces students to some important techniques for expansion,
simplification and solution of equations. Complex numbers are introduced and students will extend their
knowledge of formal proof to proof by mathematical induction, proof by contradiction and proof by
counterexample.