TAIGER Academy

For Individuals

Who is this Course for?

CS Undergraduates or Post-graduates

With A Background In Computer Science

To extend their current computer science knowledge with knowledge in AI to increase their future employability in a wide range of possiblities.

CS Professionals

Professionals With Experience In Computer Science

Computer Science Professionals can take this course to prime for a career switch to Knowledge Engineering or NLP Engineering, and replace outdated knowledge with cutting-edge technological know-how. 

Non-CS Professionals

Professionals With No Prior Experience In Computer Science

This course also caters to non-CS professionals who wish to acquire new knowledge to enhance their current career path, or re-skill to for careers in Sales, Business Development or Marketing in technology companies.

What is Learnt?

We specially customise each course to fit the needs of participants. Courses can be as short as 2 hours – for the busy professional wanting to gain and walk away with insights into what AI means for them – to a full-fledged course going deep into the technical details of artificial intelligence, with practical hands-on sessions.

Sample Syllabus

Overview Course – 2 Hours

  1. Introduction to Artificial Intelligence
    • What really is AI?
    • Why the hype?
  2. AI Ecosystem
    • Are systems intelligent?
    • AI Components
  3. The Different Disciplines of AI
    • Knowledge representation and reasoning
    • Natural Language Processing
    • Machine Learning
  4. Supervised and Unsupervised Learning
  5. How will this affect you, your career and your company?
  6. Live Demos
  7. What’s coming?
  8. Q&A Session

Extended Course

  1. Artificial Intelligence – 10 hours
    • Introduction to Artificial Intelligence
    • Knowledge representation and reasoning
    • Machine Learning
    • Treatment of Uncertainty
  2. Computational Semantics – 15 hours
    • Basic concept, Semantic Web and Linked Open Data
    • Knowledge modeling: RDF and RDFS
    • Consultation and reasoning: SPARQL and RDF entailment
    • Triplestores
    • Practical development of semantic applications
  3. Information Retrieval – 15 hours
    • Text properties and operations
    • Evaluation models and information retrieval
    • Data structures for text
    • Information retrieval on the web
  4. Natural Language Processing – 15 hours
    • Introduction to NLP
    • Language Processing: lexical, grammatical and semantic
    • Methods and tools. Modeling language and strategies: parsers, computational grammars, dictionaries and ontologies
    • Application domains: RI, Question & Answering, automated translation
  5. Semantic Search System – 5 hours
    • Semantic search system
    • Search system: annotation, indexing and search
    • Search system architecture
    • Application of computational semantics