Major: Artificial Intelligence and Smart Systems
Overview: This major focuses on the design, development, and application of AI technologies and smart systems. Students learn about machine learning, robotics, IoT (Internet of Things), and intelligent automation, preparing for careers in tech industries, research, and innovation.
Key Learning Objectives:
- Understand the fundamentals of AI, machine learning, and neural networks.
- Develop skills in programming, data analysis, and algorithm design.
- Learn techniques for building smart systems, including robotics and IoT applications.
- Explore principles of computer vision, natural language processing, and autonomous systems.
- Analyze and interpret data to create intelligent solutions.
- Develop critical thinking, problem-solving, and innovation skills for AI-driven technologies.
Core Courses:
Introduction to Artificial Intelligence
- Overview of AI history, applications, and ethical considerations.
Machine Learning
- Principles of supervised and unsupervised learning, deep learning, and reinforcement learning.
Robotics and Autonomous Systems
- Techniques for designing and programming robots and autonomous vehicles.
Internet of Things (IoT)
- Principles of IoT architecture, sensors, and smart device integration.
Computer Vision
- Techniques for image and video analysis, object detection, and pattern recognition.
Natural Language Processing (NLP)
- Principles of language models, text analysis, and chatbot development.
Smart Systems and Automation
- Design and implementation of intelligent automation in industries and daily life.
Data Science and Big Data
- Techniques for handling large datasets, data mining, and predictive analytics.
Ethics and AI Governance
- Ethical considerations, bias, privacy, and regulatory frameworks in AI.
Internship/Practicum
- Real-world experience in tech companies, research labs, or startups.
Capstone Project in AI and Smart Systems
- Comprehensive project applying skills in AI model development, robotics, or IoT solutions.
Assessment Methods:
- AI model development projects.
- Robotics and IoT prototype designs.
- Computer vision and NLP applications.
- Data science analyses and predictive modeling.
- Internship reports and presentations.
- Capstone projects on AI-driven solutions.
Recommended Resources:
- "Artificial Intelligence: A Modern Approach" by Stuart Russell and Peter Norvig.
- "Machine Learning" by Tom M. Mitchell.
- "Robotics: Modelling, Planning, and Control" by Bruno Siciliano.
- "Internet of Things: Principles and Paradigms" by Rajkamal.
- "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
Prerequisites:
- Strong foundation in mathematics (linear algebra, calculus, statistics).
- Basic programming skills (Python, Java, or C++).
- Interest in technology, innovation, and problem-solving.
Duration:
- Typically 4 years for a bachelor’s degree.
- Advanced degrees (Master’s/PhD) may take an additional 1–3 years.
Certifications:
Graduates can pursue certifications such as:
- TensorFlow Developer Certificate by Google.
- NVIDIA Deep Learning Institute Certifications.
- Microsoft Certified: Azure AI Engineer Associate.
Career Paths:
AI Engineer/Scientist:
- Developing AI models and algorithms for various industries.
Robotics Engineer:
- Designing and programming robotic systems and autonomous vehicles.
IoT Specialist:
- Creating smart devices and connected systems for homes and industries.
Data Scientist:
- Analyzing big data to extract insights and build predictive models.
AI Researcher:
- Conducting cutting-edge research in AI and machine learning.
AI Consultant:
- Advising businesses on AI integration and digital transformation.
Software Developer:
- Building AI-powered applications and platforms.
Why This Major?
- High Demand: AI and smart systems are transforming industries globally.
- Innovation-Driven: Opportunity to work on groundbreaking technologies.
- Diverse Applications: Careers in healthcare, finance, manufacturing, and more.
