NCC Short Course in Artificial Intelligence

Course Title:
NCC Short Course in Artificial Intelligence (AI)

Duration:
Up to 80 hours of learning content.

Start Dates:
Registration available throughout the year.

Awarding Institution:NCC Education

Language of Study:
English

Programme Overview:
The NCC Education Short Course in Artificial Intelligence (AI) is designed to provide a comprehensive introduction to AI, exploring both theoretical and practical aspects. The course is tailored to meet the needs of different learning cohorts, including working professionals and entry-level graduates. It aims to equip learners with essential AI knowledge and skills to apply in various business and technical contexts.

Topics Covered:

• Introduction to AI
• Problem Solving Using Search
• Knowledge Representation
• Uncertain Knowledge
• Fuzzy Logic
• Machine Learning
• Neural Networks
• Decision Trees
• Genetic Algorithms
• Expert Systems
• Natural Language Processing
• Intelligent Agents

Entry Requirements:
There are no formal prerequisites for this course. It is suitable for individuals looking to understand the basics of AI, regardless of their prior knowledge or experience.

Programme Structure:

1. Introduction to AI:
   • Definitions, History of AI, Characteristics, Limitations, Ethics, and Development.
2. Problem Solving Using Search:
   • Strategies for state space search, including uninformed and informed search.
3. Knowledge Representation:
   • Types of knowledge, logical representation, semantic networks, frame representation, and production rules.
4. Uncertain Knowledge:
   • Understanding uncertainty, probability, Bayes’ rule, and reasoning.
5. Fuzzy Logic:
   • Fuzzy logic, linguistic variables, sets and operations, rules, and systems.
6. Machine Learning:
   • Introduction to supervised, unsupervised, and reinforcement learning, and applications.
7. Neural Networks:
   • Basic structure, perceptrons, multilayer networks, learning algorithms.
8. Decision Trees:
   • Structure, terminologies, and attribute selection.
9. Genetic Algorithms:
   • Basics of genetic algorithms and natural evolution simulation.
10. Expert Systems:
    • Development, components, characteristics, and rule-based systems.
11. Natural Language Processing:
    • Terminologies, components, processing pipeline, and applications.
12. Intelligent Agents:
    • Concepts of agents, environments, rationality, and algorithms.

Learning & Teaching Strategies:
The course utilizes a blend of online learning methods:

• Video Lectures: To introduce key concepts and theories.
• Practical Activities: To apply knowledge in real-world scenarios.
• Quizzes and Assignments: To assess understanding and reinforce learning.
• Discussion Forums: For interaction with peers and instructors.

Assessment Strategy:
Assessments are conducted through quizzes, assignments, and practical activities to evaluate students’ understanding and application of AI concepts. There are no formal examinations.

Learning Outcomes:
Upon completion, students will be able to:

• Understand the importance and applications of AI.
• Apply AI search strategies and knowledge representation techniques.
• Assess techniques for reasoning with uncertain knowledge.
• Understand machine learning techniques and their applications.
• Implement and evaluate AI models and techniques in real-world problems.

Career and Professional Development:
This course prepares students for various AI career paths, including:

• AI Architect
• Business Intelligence Developer
• Big Data Engineer
• Data Scientist
• Machine Learning Engineer

Support for Student Learning:
Students will have access to:

• Learning Resources: Online study materials and libraries.
• Discussion Forums: For engagement with peers and tutors.
• Technical Support: Available throughout the course duration.

Programming Tools Used:

• WEKA
• Scikit-Learn
• Python (with NLTK)
• SWI Prolog

Total Qualification Time:
Up to 80 hours.