**Understand **the basic concepts and terminology in natural language study

**Access **and** use ** natural language corpora

**Recognize **the statistical properties of text from raw corpora

**Create** finite state automata and finite state transducers for morphological analysis of words

**Design** two level compilers for morphological analysis

**Understand **sequence classification methods

**Apply **the supervised training algorithm for HMMs for POS tagging

**Calculate **probabillities of POS tag sequences

**Apply **dynamic programming algorithms such as Viterbi to choose the best POS tag sequence for a given sentence

**Design **part-of-speech taggers from scratch

**Recognize** the limitations of context free grammars and **analyse** the extensions to CFG

**Apply **well-known parsing algorithms to natural language data

**Recognize **the limitations of parsing algorithms and improve them using differnet methods.

**Understand **the principles of natural language meaning

**Analyse **and** Compare **different representations for semantics

**Describe **state of the art methods for NLP applications such as text categorization, information extraction etc.

**Recognize **the computational problems associated to natural language recognition and parsing

**Design , implement **and** evaluate **an NLP system