1- Describes some text analysis techniques used for information retrieval
2- Describes how to build a recommender system
3- Describes the principle of K-means clustering, Decision Trees, K-NN, Naive Bayes and Neural Networks
4- Introduces WEKA software for classification and recommender systems
5- Introduces StanfordNLP framework for text analysis
What am I going to get from this course?
Learn how to:
- Use regular expressions to find and replace patterns in text.
- Use Stanford NLP framework for part-of-speech tagging and named entity recognition.
- Use GloVe tool to estimate semantic similarity between words.
- Use TF-IDF weighting scheme in a search application.
- Detect problems where clustering can be used.
- Define supervised learning problems.
- Use WEKA software to solve automatic classification problems.
- Use WEKA software to generate rules for a recommender system.
Prerequisites and Target Audience
What will students need to know or do before starting this course?
- Bachelor's Degree in computer science
- Economy, or a related specialty is recommended.
Who should take this course? Who should not?
This course is adapted for:
- A technical person willing to know how to use Artificial Intelligence techniques to solve his/her problems.
- A management person willing to understand the practical applications of Artificial Intelligence in his/her business.
- A technical or non-technical person wishing to start using existing frameworks for text analysis, clustering, automatic classification and recommender systems.
This course is not adapted for:
- A technical person wishing to understand the algorithmic details so that he/she can implement Artificial Intelligence algorithms.
- A technical person wishing to modify/improve existing Artificial Intelligence algorithms.