수학, 통계학, 데이터 사이언스, 인공지능 관련하여 공부한 것을 정리하고 있습니다.
Mathematics
Linear Algebra
UNIT I: AX = B AND THE FOUR SUBSPACES
UNIT II: LEAST SQUARES, DETERMINANTS AND EIGENVALUES
UNIT III: POSITIVE DEFINITE MATRICES AND APPLICATIONS
- LA_07: Positive Definite Matrices
- LA_08: The Singular Value Decomposition and PCA
- LA_09: Linear Transformation
- LA_10: Complex Vectors and Matrices
- LA_11: Applications
Engineering Mathematics
PART A: Ordinary Differential Equations
- EM_01: Introduction
- EM_02: 1st Order ODE
- EM_03: 2nd Order ODE
- EM_04: 2nd Order ODE - Homogeneous
- EM_05: 2nd Order ODE - Nonhomogeneous
- EM_06: Power Series Method
- EM_07: Legendre Equation
- EM_08: Frobenius Method
- EM_09: Bessel Equation
- EM_10: Laplace Transform
- EM_11: Wrap Up
- EM_12: Difference Equation
Numerical Analysis
Calculus
PART A: Single Variable Calculus
PART B: Multi Variable Calculus
Convex Optimization
Theorey I: Fundamentals
- CO_01: Introduction
- CO_02: Convexity - Sets and functions
- CO_03: Convexity - Optimization basics
- CO_04: Canonical problem forms
Algorithms I: First-order methods
- CO_05: Gradient descent
- CO_06: Subgradients
- CO_07: Subgradient method
- CO_08: Proximal gradient descent
- CO_09: Stochastic gradient descent
Statistics
Statistics 110: Probability
- ST_01: Probability and Counting
- ST_02: Conditional Probability
- ST_03: Random Variables and Their Distribution
- ST_04: Expectation
- ST_05: Continuous Random Variables
- ST_06: Moments
- ST_07: Joint Distribution
- ST_08: Transformations
- ST_09: Conditional Expectation
- ST_10: Inequalities and Limit Theorems
- ST_11: Markov Chains
Machine Learning
Bayesian Method for Machine Learning
- BM_01: Introduction to Bayesian Methods
- BM_02: Conjugate Priors
- BM_03: Latent Variable Models
- BM_04: Expectation Maximization Algorithm
- BM_05: EM Algorithm - Applications and Examples
- BM_06: Variational Inference
Learning Progress
- Andrew Ng Machine Learning 완강
- Andrew Ng Deep Learning 완강
- 선형대수학 완강
- 공학수학 A 완강
- Single Variable Calculus 정리
- Multivariable Calculus 정리
- 수리통계학
- 수치해석 중단 ⏹
- 문일철 교수님 기초 ML
- Convex Optimization (4/24) ▶️
- Kmooc - 서울대 머신러닝 데이터마이닝
- Kmooc - 이지형 교수님 강의 완강
- STAT110 완강
- Bayesian Method for ML (7/11) ▶️
- Graphical model
- Stat method for ML or Stat ML
- Learn from Kagglers
- Stanford NLP
- Intermediate Stat
- Stochastic Process
Wishlist
- Basic Measure Theory
- Analysis
Interest
Resources
- MLWIKI
- Neural Networks class
- Elements of AI
- Mathematics for CS
- 수학과 수준: Stats for Applications
- CS수준: Probabilistic Systems Analysis and Applied Probability
- Introduction to probability
- 확랜프
- 추천 책
- Probabilistic Systems Analysis and Applied Probability
http://people.duke.edu/~kh269/teaching/b551/schedule.htm
http://people.duke.edu/~kh269/teaching/b553/schedule.htm
The elements of statistical learning
https://www.coursera.org/learn/bayesian
https://datascienceschool.net/
Design and Analysis of Algorithms
https://www.youtube.com/watch?v=EzeYI7p9MjU&t=2s
https://tailwindcss.com
https://dribbble.com
https://www.coursera.org/specializations/aml?recoOrder=2&utm_medium=email&utm_source=recommendations&utm_campaign=4EV2EIu5EemdOAf9ya7Q1g
https://www.quora.com/Can-I-get-a-machine-learning-job-if-I-finish-Andrew-Ngs-Deep-Learning-Specialization
CTC
https://gogyzzz.blogspot.com/2018/08/ctc.html
칼텍 http://work.caltech.edu/lectures.html#lectures
https://www.youtube.com/playlist?list=PL0oFI08O71gKEXITQ7OG2SCCXkrtid7Fq