Difference between revisions of "Educational program"
From Research management course
(Created page with "==Data Science: A Roadmap for Bachelor, Master, and Doctoral Degrees== ===Mathematics=== * Discrete analysis and graphs *** * Abstract algebra and group theory ** * Mathemat...") |
|||
| Line 41: | Line 41: | ||
* Machine learning and data analysis | * Machine learning and data analysis | ||
* Deep learning | * Deep learning | ||
| + | * Bayesian model selection | ||
| + | * Bayesian multi-modeling | ||
* Generative models (practice BS and theory MS) | * Generative models (practice BS and theory MS) | ||
* Reinforcement and online learning | * Reinforcement and online learning | ||
| − | * Geometric deep learning | + | * Geometric deep learning |
| + | * Geometric generative models | ||
===Applications=== | ===Applications=== | ||
Revision as of 17:34, 12 February 2024
Contents
Data Science: A Roadmap for Bachelor, Master, and Doctoral Degrees
Mathematics
- Discrete analysis and graphs ***
- Abstract algebra and group theory **
- Mathematical *** and functional analysis *
- ODE, PDE, and mathematical modeling *
- Linear algebra ***
- Tensor algebra and calculus *
- Theoretical physics *
- Differential geometry and Geometric algebra *
- Scientific computation and numerical methods ***
- Measure and Probability ***
- Multivariate statistics ***
- Bayesian statistics and Graphical models **
- Stochastic processes and SDE *
- Bayesian model selection **
- Diffusion probability and flows **
Computer science
- Programming ***
- Computational differentiation **
- Software architectures **
- System analysis **
- Category theory *
- Parallel and distributed computing **
Optimization and control
- Discrete optimization **
- Convex optimization **
- Mathematical programming ***
- Optimal control**
Core of Data science
- Machine learning and data analysis
- Deep learning
- Bayesian model selection
- Bayesian multi-modeling
- Generative models (practice BS and theory MS)
- Reinforcement and online learning
- Geometric deep learning
- Geometric generative models
Applications
- Signal analysis
- Computer vision
- Audio processing
- Natural language processing
- Topic modeling and Information retrieval
- Recommender systems
- Multimedia and heterogeneous data
- Bioinformatics
- Brain-computer interfaces and metaverse
*** essential, ** recommended, * advanced
Exams
- Ph.D. theoretical minimum for Computer science: AI and machine learning