Objectives:

1. Being able to identify machine learnming applications

2. Being able to program machine learning algorithms

3. Being able to create a machine learning application

4. Being able to improve the performance of a machine learning application

 

ML APP in our everyday life:

(Automatic Diagnosis of Diseases 自动疾病诊断)

(Artificial Neural Networks-ANN 智能神经网络)

1. Voice search

2. Handwritten character recognition

3. Face detection and face recognition

4. Fingerprint verification

 

Definition of ML

1. ML is a set of methods that can automatically detect patterns模式 in data,

and then use the uncovered无覆盖的 patterns to predict future data.

Example：Face detection

 

2. ML is concerned with the question of how to construct the computer programs that 

automatically improve themselves with experience.

Example:  Automatic diagnosis of diseases

 

3. We have a model defined up to some parameters, and learning is the execution of a 

computer program to optimize the parameters of the model using the training data.

Example: Artificial neural networks

 

4. The model may be predictive to make predictions in the future, or descriptive to gain

knowledge from data, or both.