By the end of this course, you will be able to:

• Illustrate how quantum entanglement enables quantum computers to perform complex tasks
• Analyze the differences between classical and quantum computing in information processing and computation
• Define key concepts of quantum computing: qubits, superposition, and entanglement
• Explain how quantum computers solve problems beyond the reach of classical computers
• Identify real-world quantum computing applications in cryptography and optimization
• Evaluate the potential impact of quantum computing on finance and healthcare
• Compare qubits with classical bits and their unique properties
• Describe the quantum mechanics principles foundational to quantum computing
• Estimate the challenges and limitations of quantum computing in practical applications

By the end of this course, you will be able to:

• Create stories, images, and music with AI tools like ChatGPT, DALL·E, and Suno
• Apply AI creatively in writing, art, and music through hands-on projects
• Analyze how AI models generate content and understand their limitations, strengths, and potential for innovation
• Evaluate the quality, originality, and ethical impact of your AI-generated creations and those of others
• Generate new ideas and multimedia content by interacting with intelligent systems in a creative workflow
• Illustrate your learning by building a portfolio that showcases your AI-powered creative projects

By the end of this course, you will be able to:

• Analyze the ethical implications of AI technologies
• Apply psychological theories to human-AI interactions
• Assess the impact of algorithmic bias on decision-making
• Define key concepts related to AI and ethics
• Evaluate the effectiveness of emotional AI in improving user experience
• Identify the psychological effects of AI on human behavior

By the end of this course, you will be able to:

• Analyze the fundamental role of mathematics in quantum computing
• Explain complex numbers use in quantum wave functions
• Illustrate quantum states using vectors, matrices, and Dirac notation
• Apply linear algebra to compute inner products and describe qubit operations
• Contrast multi-qubit systems with tensor products and density matrices
• Evaluate entanglement and Bell states to understand quantum correlations
• Describe the Quantum Fourier Transform in period-finding and quantum algorithms
• Explain the mathematical structure behind Shor’s and Grover’s algorithms
• Analyze error correction using stabilizer formalism, syndromes, and parity checks

By the end of this course, you will be able to:

• Define key psychological principles that influence chatbot design and human–machine interaction
• Explain how cognitive biases, emotions, and communication theories shape user perceptions of chatbots
• Analyze real-world case studies of chatbots in education, healthcare, and commerce
• Evaluate chatbot conversations by identifying strengths, weaknesses, and psychological impacts
• Compare human communication with chatbot interaction to highlight similarities and differences
• Assess the ethical and psychological challenges linked to the future of chatbots

By the end of this course, you will be able to:

• Define the fundamental concepts and principles of Artificial Intelligence
• Explain the differences between AI, Machine Learning, and Deep Learning
• Apply machine learning algorithms to solve practical problems
• Analyze datasets to extract insights and prepare data for AI models
• Evaluate the performance of AI models using appropriate metrics
• Create simple neural networks for vision and language tasks
• Discuss ethical issues and societal impacts of Artificial Intelligence
• Plan a personal roadmap to pursue careers in high-demand AI roles

By the end of this course, you will be able to:

• Define the core principles of human behavior analysis and its connection with Artificial Intelligence
• Identify the different data sources and modalities used to capture human behavior
• Analyze Artificial Intelligence methods such as machine learning, natural language processing, and computer vision
• Apply AI techniques to real-world contexts in healthcare, education, and business
• Evaluate the challenges, risks, and limitations of AI in behavior analysis
• Discuss the ethical considerations and propose responsible solutions for AI in human behavior analysis

By the end of this course, you will be able to:

• Define Affective Artificial Intelligence and its link to psychology and human–computer interaction
• Describe the history and foundations of affective computing
• Identify models of human emotions and how they are used in AI systems
• Analyze techniques for emotion recognition in facial, speech, and text data
• Compare algorithms and evaluate their strengths and weaknesses
• Illustrate real-world systems such as chatbots and robots using affective AI
• Recognize risks, privacy concerns, and regulation needs
• Relate affective AI skills to careers in AI, psychology, and industry