Artificial Intelligence
Introduction to Artificial Intelligence
Welcome to Introduction to Artificial Intelligence, where we embark on an exhilarating journey into the realm of intelligent systems, algorithms, and their transformative impact on our world. In this course, we delve into the fundamental principles, applications, and ethical considerations of AI, equipping you with the knowledge and skills to navigate this rapidly evolving field. Get ready to unlock the
mysteries of machine learning, explore the wonders of natural language processing, and discover the endless possibilities of robotics. Whether you’re a seasoned enthusiast or a curious beginner, join us as we embark on this captivating exploration of Artificial Intelligence.
This course provides an introduction to the field of Artificial Intelligence (AI), covering its foundational concepts, techniques, applications, and societal implications. Through lectures, discussions and hands-on exercises, students will gain a broad understanding of AI and its relevance in various domains
Course Objective
- Understanding the Basics: Provide students with a fundamental understanding of artificial intelligence, including its definition, scope, and various subfields.
- History and Evolution: Explore the historical development of AI, from its inception to modern-day applications, highlighting key milestones, breakthroughs, and influential figures.
- Core Concepts and Techniques: Introduce students to essential AI concepts and techniques, such as machine learning, neural networks, natural language processing, computer vision, and robotics.
- Problem Solving with AI: Equip students with problem-solving skills using AI techniques, including algorithm design, optimization, and heuristic methods.
- Ethical and Societal Implications: Raise awareness of ethical considerations and societal impacts associated with AI technologies, including issues related to bias, fairness, privacy, and job displacement.
- Practical Applications: Illustrate real-world applications of AI across various domains, including healthcare, finance, transportation, and entertainment, to demonstrate the breadth and versatility of AI.
- Hands-on Experience: Provide hands-on experience through programming assignments, projects, or simulations, allowing students to apply AI algorithms and tools to solve practical problems.
- Critical Thinking and Evaluation: Develop students; ability to critically evaluate AI systems, including their strengths, limitations, and potential risks, fostering a deeper understanding of AI’s capabilities and challenges.
- Collaboration and Communication: Encourage collaboration and communication skills through group projects, discussions, and presentations, reflecting the interdisciplinary nature of AI and the importance of teamwork in solving complex problems.
- Future Trends and Directions: Discuss current trends and future directions in AI research and development, including emerging technologies, challenges, and opportunities, to prepare students for ongoing learning and career advancement in the field.
Artificial Intelligence Course Outline
Definition and scope of AI
Historical overview and key milestones
Ethical considerations in AI development and deployment
Problem-solving agents
Search algorithms: uninformed and informed search
Heuristic evaluation and optimization
Basic concepts of machine learning
Supervised, unsupervised, and reinforcement learning
Applications of machine learning in AI
Introduction to artificial neural networks
Deep learning architectures: CNNs, RNNs, and their applications
Training neural networks and hyperparameter tuning
Basics of natural language processing (NLP)
Text preprocessing, tokenization, and feature extraction
NLP applications: sentiment analysis, named entity recognition, and machine translation
Introduction to computer vision
Image preprocessing and feature extraction
Computer vision applications: object detection, image classification, and facial recognition
Fundamentals of robotics
Robot perception, cognition, and action
Robotic applications: autonomous navigation, manipulation, and human-robot interaction
Bias, fairness, and transparency in AI
Privacy concerns and data ethics
AI’s impact on employment, economy, and society
Healthcare: medical imaging, disease diagnosis, and personalized medicine
Finance: fraud detection, algorithmic trading, and risk assessment
Transportation: autonomous vehicles, traffic optimization, and predictive maintenance
Emerging trends in AI research and development
Challenges and opportunities in the field
Reflections on the course and future directions
Trainetics Academy Course Schedule
| Artificial Intelligence Course | Date | Course duration | Physical / Online Fee | Funding Support | Location |
|---|---|---|---|---|---|
| AI Course-2911-Fri | Fri, 29 Nov | 8 hours ( 9am to 6pm) | $480 | SkillsFuture Credits $480 / UTAP $250 | City Centre 137 Cecil Street |
| AI Course-2212-Sun | Sun, 22 Dec | 8 hours ( 9am to 6pm) | $480 | SkillsFuture Credits $480 / UTAP $250 | City Centre 137 Cecil Street |
| AI Course-2312-Mon | Mon, 23 Dec | 8 hours ( 9am to 6pm) | $480 | SkillsFuture Credits $480 / UTAP $250 | City Centre 137 Cecil Street |
