SIAI Lecture Notes
These documents are a part of lecture notes from Swiss Institute of Artificial Intelligence (SIAI), a higher educational institution under GIAI‘s direct management. Lecture notes are from BSc Data Science, MBA AI/BigData, MBA AI/Finance, and MSc AI/Data Science programmes. Our researchers are encouraged to teach at SIAI and help young students to be a mature researcher in this field. Contents on this website, GIAI Books, are only selected parts of SIAI’s in-class discussions, due to limitations in our time and effort for writing self-contained books. Therefore we suggest that you be careful on referencing the contents.
All errors are to blame us and please help us to fix them. You can reach us on GIAI Square, an open community platform for academic minds in any related fields of AI/Data Science. We believe the new discipline is not limited to a handful of university majors but is indebted to a variety of STEM majors that share common mathematical and statistical tools. Along with support to SIAI Admission, the open platform is designed to share knowledge, support discussions, and sharpen your research skills by peer learning.
We hope below incomplete notes become the seed of a giant sequoia.
Articles
- Math & Stat for MBA I
- Class 1. Basic statistics
- Class 2. Basic statistics
- Class 3. Basic statistics
- Class 4. Basic time series regression analysis
- Class 5. Endogeneity – Correlation between errors and regressors
- Class 6. Endogeneity 2 – Correlation between errors and regression
- Class 7. Summary of Math & Stat for MBA I
- Class 8. Summary of Math & Stat for MBA I
- Math & Stat for MBA II
- Class 1. Large sample properties and maximum likelihood
- Class 2. Generalized least square
- Class 3. Time Series – PCF and PACF
- Class 4. Time series II – Seasonality and frequency
- Class 5. Simulation and Bayesian
- Class 6. Bayesian statistics
- Class 7. Summary of Math & Stat for MBA II
- Class 8. Summary of Math & Stat for MBA II
- Data-based Decision Making
- Class 1. Comparative advantage
- Class 2. Consumer choices
- Class 3. Causality in data science
- Class 4. ANOVA to Multivariate regression
- Class 5. Probability models
- Class 6. Endogeneity – Measurement error
- Class 7. Summary of Data-based Decision Making 1
- Class 8. Summary of Data-based Decision Making 2
- Scientific Programming
- Machine Learning
- Deep Learning
- Reinforcement Learning
- Class 1. Introduction to intertemporal optimization
- Class 2. Bellman equations
- Class 3. Hamiltonian analysis
- Class 4. First-order differential equations (FODE)
- Class 5. Value-based optimization
- Class 6. Policy-based optimization
- Class 7. Large Language Model (LLM)
- Class 8. Summary of Reinforcement Learning
- Data Visualization
- Data Management
- AI in Digital Marketing
- AI Business Cases
- Law and AI
- AI in Corporate Finance
- AI in Asset Management
- Regression Analysis I
- Regression Analysis II
- Regression Analysis III
- Bayesian Statistics I
- Bayesian Statistics II
- Panel Data Analysis
- Game Theory
- Games with Incomplete Information
- Social Network Analysis
- Probabilistic Graphical Models
- Information Theory
- Advanced Deep Learning
- Advanced Reinforcement Learning
