The Future in : ML and NLP
Machine learning is a core sub-area of artificial intelligence (AI)
that provides system, the ability to automatically learn and improve
from experience without being explicitly programmed. Machine
learning focuses on the development of computer programs that
can access data and use it learn for themselves. With the constant
evolution of the field, it has become the most relevant technoloigical
breakthrough of recent time. Yet, Lack of expert resources is the
biggest challenge the world is facing. Natural Language Processing
or Computational Linguistics (NLP / CL) is a subfield of Artificial
Intelligence that is one of the most relevant technologies of the
While natural language processing isn’t a new science, the technology is rapidly advancing due to an increased interest in human to machine communications, availability of big data, powerful computing and enhanced algorithms. NLP systems can perform many useful tasks such as extracting knowledge from text, machine translation and conversational agents, by analyzing and manipulating language . The objective of the winter school is to introduce the basic background in NLP and relevant ML methods as well as to introduce them to some exhilarating research in this area.
Eminent speakers from academia as well as industry will deliver talks and will interact with the students. ICFOSS seeks to establish a platform to endorse the research activities in the area and to expand the skills of the participants through the programme . We intent to promote and support the involvement of women in FOSS communities as well as in the field of Natural Language Processing (NLP). The event includes invited talks and hands-on sessions and provide an opportunity for the participants to acquire individual attention of mentors and to develop network between themselves. The school shall focus to provide hands-on experience to boost the capability and potential of the participants. The course will present the basic NLP problems such as Part-Of-Speech Tagging and Named Entity Recognition and will pay attention to some approaches for Speech Recognition, Speech Synthesis and Machine Translation . The practical part of the course includes working with NLTK, gensim, scikit-learn, NumPy, spaCy, and SciPy for solving these problems. Programming skills are welcome, but not strongly required. We will render introductory knowledge of Python to help the participant s in doing their computations on their own, while taking in account to each of their individual skills and interests.