Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP

This course is aimed at developers and data scientists who wish to understand and implement AI within their applications. Special focus is given to Data Analysis, Distributed AI and NLP.

1. Distribution big data
   1. Data mining methods (training single systems + distributed prediction: traditional machine learning algorithms + Mapreduce distributed prediction)
   2. Apache Spark MLlib
2. Recommendations and Advertising:
   1. Natural language
   2. Text clustering, text categorization (labeling), synonyms
   3. User profile restore, labeling system
   4. Recommended algorithms
   5. Insuring the accuracy of "lift" between and within categories
   6. How to create closed loops for recommendation algorithms
3. Logical regression, RankingSVM,
4. Feature recognition (deep learning and automatic feature recognition for graphics)
5. Natural language
   1. Chinese word segmentation
   2. Theme model (text clustering)
   3. Text classification
   4. Extract keywords
   5. Semantic analysis, semantic parser, word2vec (vector to word)
   6. RNN long-term memory (TSTM) architecture