Scientific Programming
Related articles
- Class 5. Probability models
- Class 6. Factor analysis
- Bayesian Statistics II
- Games with Incomplete Information
- Jeonghun Song (MSc, 2023)
- Class 7. Artificial Neural Network
- Class 8. Summary of Math & Stat for MBA I
- Class 3. Causality in data science
- Class 6. Recurrent Neural Network
- Machine Learning
- Class 4. Ensemble
- Bayesian Statistics I
- Class 1. Basic statistics
- Class 7. Deep Generative Models
- Class 8. Summary of Reinforcement Learning
- Hyeyoung Park (MBA, 2023)
- Class 6. Variance reduction
- Class 3. MCMC and Bayesian extensions
- Data-based Decision Making
- Class 4. Basic time series regression analysis
- Class 8. Summary of Machine Learning
- Game Theory
- Class 7. Summary of Math & Stat for MBA I
- SIAI Yearbook
- Class 6. Bayesian statistics
- Class 4. Boltzmann machine
- Class 7. Summary of Data-based Decision Making 1
- Jeongwoo Park (MSc, 2023)
- Donggyu Kim (MBA, 2024)
- Advanced Reinforcement Learning
- Class 3. Data grouping
- Class 1. Comparative advantage
- AI in Asset Management
- Hyoung Keun Kwon (MSc, 2024)
- Class 6. Endogeneity 2 – Correlation between errors and regression
- Class 4. Time series II – Seasonality and frequency
- AI in Corporate Finance
- Class 8. Summary of Scientific Programming
- Mincheol Kim (MSc, 2023)
- Sungsu Han (MBA, 2024)
- Data Visualization
- Class 3. Basic statistics
- Class 3. Time Series – ACF and PACF
- Class 8. Summary of Data-based Decision Making 2
- Class 5. Simulations
- Law and AI
- Class 5. Simulation and Bayesian
- Class 2. Classification
- Class 4. ANOVA to Multivariate regression
- SIAI Yearbook – 2023
- Class 1. Introduction to deep learning
- Class 2. Exact matching via quadrature
- Probabilistic Graphical Models
- SIAI Yearbook – 2024
- Class 8. Summary of Deep Learning
- Social Network Analysis
- Deep Learning
- Class 4. First-order differential equations (FODE)
- Class 2. Autoencoder
- Class 2. Generalized least square
- Class 7. Optimization methods
- Class 2. Basic statistics
- Math & Stat for MBA I
- Data Management
- AI Business Cases
- SIAI Lecture Notes
- Panel Data Analysis
- Class 6. Endogeneity – Measurement error
- Regression Analysis I
- Class 5. Dimensionality reduction
- Class 8. Summary of Math & Stat for MBA II
- Class 5. Value-based optimization
- Reinforcement Learning
- Class 1. Large sample properties and maximum likelihood
- Yeonsook Kwak (MBA, 2024)
- Class 2. Bellman equations
- Class 5. Endogeneity – Correlation between errors and regressors
- Class 5. Image recognition
- Regression Analysis II
- Class 1. Approximations and computational cost
- Class 7. Large Language Model (LLM)
- Bohyun Yoo (MBA, 2023)
- Books
- AI in Digital Marketing
- Class 7. Summary of Math & Stat for MBA II
- Information Theory
- Regression Analysis III
- Scientific Programming
- Class 1. Introduction to intertemporal optimization
- Class 3. Hamiltonian analysis
- Class 3. Fourier transform
- Math & Stat for MBA II
- Class 1. Regression problems
- Advanced Deep Learning
- Class 6. Policy-based optimization
- Class 2. Consumer choices
-
See More
