01
Learning Objectives
Provide fundamental knowledge of Artificial Intelligence, understand Machine Learning models and evaluation, and develop awareness of ethical and regulatory aspects.
FINAL EXAM ONLY
AI_INTRO_LA
Introduction to Artificial Intelligence (AI) – Exam
The course "Introduction to Artificial Intelligence (AI) – Exam" provides a thorough overview of the theoretical and practical foundations of Artificial Intelligence. It covers key definitions and fields, including Machine Learning and Deep Learning. The curriculum explores knowledge representation, search and problem-solving techniques, along with basic logic and reasoning principles. The introduction to Machine Learning addresses both supervised and unsupervised methods, focusing on model evaluation using train/test splits, overfitting control, and performance metrics. Students also receive insights into neural networks and Deep Learning, supported by application examples in Natural Language Processing, Computer Vision, and recommender systems. The course discusses the ethical considerations, bias, privacy, and security issues related to AI usage. Finally, it presents main tools and typical workflows, covering datasets, preprocessing, training, and deployment. Passing the exam certifies understanding of core concepts and the ability to apply them to real-world business scenarios.
Exam duration60 min
Pass mark70%
Available questions200
CIWZ PROFESSIONAL ASSESSMENT
High-integrity learning environment
The pathway is designed for managers, executives, function owners, auditors and professionals. Lessons use validated minimum study time and a contextual tutor; assessments use server-side random selection of questions and answers, session-continuity controls, camera and local audio-activity monitoring during protected tests.
Traceability and reliability: integrity events are logged for authorized review; automated signals do not determine the result on their own.
OFFICIAL PATHWAY INFORMATION
Course technical sheet
Purpose, references, delivery method, commercial terms and certificate value, presented clearly and transparently.
02
Skills Acquired
- Definitions and scope of AI, ML, and DL
- Search and problem-solving methodologies
- Basic logic and reasoning
- Supervised and unsupervised Machine Learning techniques
- Model evaluation and overfitting management
- Introductory knowledge of neural networks and Deep Learning
- Practical applications in NLP, Computer Vision, and recommender systems
- Analysis of ethics, bias, privacy, and security issues
- Preparation of AI workflows: datasets, preprocessing, training, deployment
03
Target Audience
Professionals, students, and enthusiasts interested in AI fundamentals and passing a related certification exam.
04
Prerequisites
Basic knowledge of computer science and statistics; familiarity with elementary mathematical concepts.
05
Program
- Fundamentals of AI, ML, and DL
- Knowledge representation
- Uninformed and informed search
- Logic and reasoning
- Machine Learning: supervised and unsupervised
- Model evaluation: train/test, overfitting, metrics
- Introduction to neural networks and Deep Learning
- Practical applications
- Ethical and security issues
- AI workflows and tools
06
Teaching Methodology
Lectures, verification exercises, case studies, and simulated exam questions to reinforce learning.
07
Assessment Method
60-minute exam with passing required at 70% correct answers.
08
Duration
60 minutes (exam).
09
Certification
Certificate of exam passing available upon payment of certification fee.
10
Expected Outcomes
Ability to understand and distinguish fundamental AI concepts and apply them in practical contexts with attention to risks and ethical implications.