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- LLM 6 - Instruction Fine-Tuning and PEFT
- LLM 5 - Pretraining, Scaling, and Domain Models
- LLM 3–4 - Decoding, Prompt Engineering, and RAG
- Deep Learning 3 - Neural Language Models Deep Learning 2 - Multilayer Perceptrons
- Options 101 - Market Making, Pricing, and Greeks
- 2 - Competition Techniques
- LLM 2 - Attention and Transformers
- Database 2 - ER Modeling and Relational Algebra
- 4 - 线性、二次与锥优化 4 - Linear, Quadratic & Conic Optimization 5 - 高阶凸优化 5 - Advanced Convex Optimization 6 - 多目标与鲁棒优化 6 - Multi-Objective & Robust Optimization 7 - 智能优化方法 7 - Intelligent Optimization 8 - Pose Graph Optimization 9 - Differential Equations 101 10 - Differential Equations II 11 - Advanced Differential Equations 12 - Differential-Equation Competition Cases 13 - Time Series 101 14 - Advanced Time Series 15 - Data Preparation 101 16 - Advanced Data Analysis 17 - Evaluation Models 101 18 - Financial Market Volatility 19 - Writing the Abstract 20 - Writing the Main Text 21 - Competition Mindset & Preparation
- 1 - 从现实问题到数学模型 1 - From Reality to a Model 3 - Visual Evidence 3 - The Abstract 4 - Assumptions and Architecture 5 - Results and Validation 6 - Convex Optimization 7 - Trade-offs and Uncertainty 8 - Genetic Algorithms 9 - Swarm and Annealing 10 - Differential Equations 11 - Populations and Compartments 12 - Time Series Foundations 13 - Forecasting Models 14 - Data Foundations 15 - Regression and Inference 16 - Dimension Reduction and Clustering 17 - Evaluation Models 18 - Competition Studio
- LLM 1 - Fundamentals of Deep Learning
- Database 1 - Systems, Models, and SQL Deep Learning 1 - Single-Layer Models
2025 1
2024 6