National College of Ireland
National College of Ireland

MSC IN ARTIFICIAL INTELLIGENCE

National College of Ireland, Ireland

Course Overview

This program is designed to equip students with the knowledge and skills needed to understand, develop, and implement AI solutions across various industries. It covers both the theoretical foundations and practical applications of AI, preparing graduates for roles in research, development, and leadership within the rapidly evolving AI landscape.

Course Type
PG
Course Nature
Full Time
Course Duration
1 Year
Total Fee
€15000
Intake
September 2024 , January 2024
Language Proficiency

  • DEGREE
  • IELTS

Documents Required
  • 10TH
  • 12TH
  • DEGREE
  • DEGREE PROVISSIONAL CERTIFICATE
  • Degree Consolidated Marksheet
  • Degree Individual Marksheet
  • PASSPORT
  • LOR 1
  • LOR 2
  • MOI
  • CV
  • SOP
University
National College of Ireland
University Details

The National College of Ireland (NCI) is a non-profit state-supported advanced education establishment in Dublin's city centre. NCI receives state financing as a core grant directly from the Department of Education and Skills. It has strong relations with incredibly famous US colleges. For instance, the M.Sc. in Cloud Computing has been created with educators from Stanford, UC Berkeley, and Cornell. The NCI School of Computing is one of the largest schools in Ireland and sits in the centre point of Dublin which is known as the Silicon Valley of Europe. Within walking distance of NCI are the European Headquarters of Facebook, LinkedIn Google, and Twitter. It is with these organizations that NCI has created courses and where NCI graduates acquire employment. The School of Business sits in the center point of Dublin's International Financial Services Centre. The world's leading banks and consultancy organizations are inside the reach of the campus.

Syllabus
  1. Foundations of Artificial Intelligence:

    • Principles and concepts of AI.
    • Problem-solving using AI techniques.
  2. Machine Learning:

    • Supervised and unsupervised learning.
    • Deep learning and neural networks.
    • Reinforcement learning.
  3. Data Science and Big Data:

    • Data preprocessing and feature engineering.
    • Handling and analyzing large datasets.
    • Data visualization and interpretation.
  4. Natural Language Processing (NLP):

    • Understanding and processing human language by machines.
    • Sentiment analysis, language generation, and text summarization.
  5. Computer Vision:

    • Image and video analysis.
    • Object recognition and tracking.
    • Image classification and segmentation.
  6. Robotics and Autonomous Systems:

    • Applications of AI in robotics.
    • Autonomous navigation and decision-making.
  7. Ethics and Responsible AI:

    • Ethical considerations in AI development and deployment.
    • Bias and fairness in AI algorithms.
    • Societal impacts of AI.
  8. AI Applications:

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