1、培訓過程中，如有部分內容理解不透或消化不好，可免費在以后培訓班中重聽；
        2、培訓結束后,授課老師留給學員聯系方式,保障培訓效果,免費提供課后技術支持。
        3、培訓合格學員可享受免費推薦就業機會。

Supervised learning: classification and regression

Bias-variance trade off
Logistic regression as a classifier
Measuring classifier performance
Support vector machines
Neural networks
Random forests
Unsupervised learning: clustering, anomaly detetction

principal component analysis
autoencoders
Advanced neural network architectures

convolutional neural networks for image analysis
recurrent neural networks for time-structured data
the long short-term memory cell
Practical examples of problems that AI can solve, e.g.

image analysis
forecasting complex financial series, such as stock prices,
complex pattern recognition
natural language processing
recommender systems
Software platforms used for AI applications:

TensorFlow, Theano, Caffe and Keras
AI at scale with Apache Spark: Mlib
Understand limitations of AI methods: modes of failure, costs and common difficulties

overfitting
biases in observational data
missing data
neural network poisoning