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方案一
USAAIO初级(60小时）
| 章节 | 内容 | 课时(小时） |
| 微积分 | 1. Intro to calculus2. Derivatives3. Max and Min and second derivative4. The exponential functions5. Integrals | 10 |
| 线性代数 | 1. The geometry of linear equations2. Elimination with matrices3. Multiplication and inverse matrices4. Factorization into A=LU5. Transposes, permutations, spaces R^n6. Column space and nullspace7. Independence, basis, and dimension8. Orthogonal matrices and Gram-Schmidt9. Determinant formulas and cofactors10. Cramer’s rule, inverse matrix and volume11. Eigenvalues and eigenvectors | 20 |
| Python基础 | 1. Variable, expressions and statements2. Functions3. Conditionals and recursion4. Fruitful functions5. Iteration6. String7. Lists8. Tuples9. Dictionaries10. Classes and objects11. Classes and functions12. Classes and methods | 20 |
| Python进阶 | Numpy, pandas, matplotlib | 4 |
| 机器学习 | 1. linear regression | 6 |
USAAIO中级(40小时）
| 章节 | 内容 | 课时(小时） |
| 机器学习 | Logistic regression | 6 |
| 深度学习 | 1. Neural network2. Transformer | 8 |
| 机器视觉 | 1. Convolutional neural network2. Object detection3. Unet4. Generative adversarial network | 14 |
| 自然语言处理 | generative AI | 10 |
USAAIO高级(40小时）
| 章节 | 内容 | 课时(小时） |
| 机器学习 | 1. support vector machine2. ensemble learning3. bias-variance tradeoff4. cross-validation5. loss functions6. k-means clustering7. principal component analysis | 28 |
| 扩散生成模型 | 1. Denoising diffusion probabilistic models2. Stable diffusion | 6 |
| 自编码器 | 1. Autoencoder2. Variational autoencoder | 6 |
方案二
USAAIO初级(50小时）
| 章节 | 内容 | 课时(小时） |
| Python基础 | 1. Variable, expressions and statements2. Functions3. Conditionals and recursion4. Fruitful functions5. Iteration6. String7. Lists8. Tuples9. Dictionaries10. Classes and objects11. Classes and functions12. Classes and methods | 30 |
| Python进阶 | Numpy, pandas, matplotlib | 4 |
| 机器学习入门 | 1. linear regression2. Logistic regression3. decision trees4. kNN5. k-means clustering | 16 |
USAAIO中级(50小时)
| 章节 | 内容 | 课时(小时） |
| 微积分 | 1. Intro to calculus2. Derivatives3. Max and Min and second derivative4. The exponential functions5. Integrals | 10 |
| 线性代数 | 1. The geometry of linear equations2. Elimination with matrices3. Multiplication and inverse matrices4. Factorization into A=LU5. Transposes, permutations, spaces R^n6. Column space and nullspace7. Independence, basis, and dimension8. Orthogonal matrices and Gram-Schmidt9. Determinant formulas and cofactors10. Cramer’s rule, inverse matrix and volume11. Eigenvalues and eigenvectors | 20 |
| 机器学习进阶 | 1. linear regression2. Logistic regression3. decision trees4. kNN | 8 |
| 机器学习 | 1. support vector machine2. ensemble learning3. bias-variance trade-off4. cross-validation5. loss functions6. principal component analysis | 12 |
USAAIO高级(50小时)
| 章节 | 内容 | 课时(小时） |
| 深度学习 | 1. Neural network2. Convolutional neural network3. Transformer | 12 |
| 扩散生成模型 | 1. Denoising diffusion probabilistic models2. Stable diffusion | 8 |
| 自编码器 | 1. Autoencoder2. Variational autoencoder | 8 |
| 机器视觉 | 1. Object detection2. Unet3. Generative adversarial network | 12 |
| 自然语言处理 | generative AI | 10 |
更有一对一专属备考规划！
