Student's Portal

The Course Covers Algorithms For The Understanding of Texts Written In Human Language. Among The Covered Topics Are# Tagging Tasks (part of Speech and Named Entity), Syntactic Parsing and Lexical Semantics. The Course Will Put an Emphasis On Machine Learning Algorithms For Structured Learning and Probabilistic Graphical Models. Learning Outcomes# At The End of The Course The Student Will Know# 1. to Formulate The Various Tasks an Algorithm Should Solve in Order To Gain Understanding of a Text. 2. to Design and Implement in Software Algorithms For Markovian Language Models. 3. to Design and Implement in Software Algorithms For Sequential Tagging Tasks Such As Part of Speech (pos) Tagging and Named Entity Recognition (ner). 4. to Design and Implement in Software Algorithms For Learning Complex Structures (such As Trees) in Order to Solve Problems Such As Statistical Syntactic Parsing (dependency As Well As Phrase Structure Parsing). 5. to Design and Implement Algorithms That Learn The Semantics Of Words, Phrases and Sentences in The Vector Space Modeling Framework. 6. to Design and Implement Dynamic Programming Algorithms, Algorithms For Inference in Graphical Models and Parameter Estimation Algorithms In Order to Solve Sentence and Text Structure Learning Problems.

Faculty: Data and Decision Sciences
|Undergraduate Studies |Graduate Studies

Pre-required courses

46195 - Machine Learning or 46202 - Data Analysis or 46203 - Planning and Reinforcemet Learning or 96411 - Machine Learning 1 or 236756 - Introduction to Machine Learning

Course with no extra credit

97216 - Natural Language Processing 236299 - Intr. to Natural Language Processing

• Understanding machine learning : from theory to algorithms - Shalev-Shwartz, Shai