Machine learning @ Peijian

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Machine learning @ Peijian by Mind Map: Machine learning @ Peijian

1. System design

1.1. Feature Scaling

1.1.1. mean normalization

1.1.2. direct normaliation

1.2. Feature manipulation

1.3. Regularization

1.4. Bias & Varaiance

1.4.1. Learning curve figure

1.4.2. Error vs model figure

1.4.3. Error vs regularization figure

1.5. Evaluation & diagonsis

1.6. Cross validation & test

1.6.1. test design for anomaly

1.7. Precision & recall, F1-force score

1.8. Large data rationale

1.9. How to choose from techniques

1.10. One-vs-all

1.11. Map reduce & Data parallelism

1.12. Ceiling analysis

2. Unsupervised learning

2.1. Clustering

2.1.1. k-means alogrihtm Centroids Number of clusters

2.2. Dimension Reduction (Feature reduction)

2.2.1. Principal Component Analysis (PCA) Algorithm process How to choose k: variance retataince Data reconstruction

2.3. Anormaly Detection

2.3.1. Independent Gaussian Hypothesis Anomaly model and algorithm

2.3.2. Multivariate Gaussian distribution Multivariate Gaussian distribution

3. Supervised learning

3.1. Linear regression

3.1.1. Hypothesis Fuction

3.1.2. Cost function

3.1.3. Gradient descent

3.1.4. Normal equation solution

3.2. Classification

3.2.1. Logistic regression Hypothesis with sigmoid function Decision boundary Linear Non-linear Cost fuction Gradient descent

3.2.2. Neural Networks Hypothesis: Neuron model based on logistic unit Forward propagation Cost function Back propagation

3.2.3. Support vector machine Hypothesis Kernels: constructed new featrues SVM library

3.2.4. Collaborative filtering Feature and example exchanging perspctive

3.2.5. Online learning Discard examples Mini-batch gradient descent Stochastic gradient descent

4. Actual Systems (under development)

4.1. Recommender system

4.2. Photo optical character recognition