TSC1005 - Introduction to Artificial Intelligence

COURSE DESCRIPTION

Introduction to Artificial Intelligence introduces learners to the foundations, terminology, applications, limitations, and responsible adoption of artificial intelligence. Four connected topics anchor the course: Defining Artificial Intelligence; How AI Functions; AI in Everyday Life and Industry; and Building Conceptual Foundations. This progression helps students connect foundational concepts, professional methods, analytical judgment, and applied decision-making in realistic technology and organizational environments.

Scenario-based exercises and a final applied task move the material from explanation to workplace use. Throughout the course, students practice defining AI, explaining data-driven learning, recognizing industry applications, distinguishing capabilities from limitations, and evaluating responsible-use concerns, producing an industry-impact report that evaluates an AI application, its benefits, its risks, and responsible practices that can be discussed in exam-style questions and transferred to professional assignments.

KEY POINTS

  1. Defining Artificial Intelligence: Establishes the terminology, principles, and professional context needed to understand the topic’s significance and its connection to sound decisions.
  2. How AI Functions: Examines practical workflows, tools, and standards that support consistent, efficient, and defensible execution.
  3. AI in Everyday Life and Industry: Develops the ability to recognize risks or constraints, compare alternatives, and select appropriate controls, methods, or responses.
  4. Building Conceptual Foundations: Connects course knowledge to planning, documentation, measurement, collaboration, and continuous improvement.
  5. Applied Practice: Uses scenarios and practical exercises to produce an industry-impact report that evaluates an AI application, its benefits, its risks, and responsible practices and to justify decisions with evidence and accepted professional practice.
  6. Professional Standards and Readiness: Reinforces AI literacy, ethical judgment, transparency, fairness, accountability, and evidence-based communication for exam-style review and workplace application.

CORE LEARNING OUTCOMES

  1. Foundational Knowledge: Explain the principles, purpose, and professional significance of Topic 1, “Defining Artificial Intelligence,” within the foundations, terminology, applications, limitations, and responsible adoption of artificial intelligence.
  2. Methods and Tools: Apply structured methods, appropriate tools, and accepted practices to the work addressed in Topic 2, “How AI Functions.”
  3. Analysis and Judgment: Analyze the conditions presented in Topic 3, “AI in Everyday Life and Industry”; identify significant risks or opportunities; and select a defensible response.
  4. Solution Development: Develop and justify a practical approach to Topic 4, “Building Conceptual Foundations.”
  5. Applied Deliverable: Produce an industry-impact report that evaluates an AI application, its benefits, its risks, and responsible practices that demonstrates accurate, ethical, and well-documented application of course concepts.
  6. Professional Communication: Communicate findings and recommendations using terminology, documentation standards, and evidence appropriate to technology and organizational environments.
TSC1010 - Data and Information for AI Systems

COURSE DESCRIPTION

Data and Information for AI Systems introduces learners to the collection, organization, preparation, quality, and ethical governance of data used by artificial intelligence systems. Four connected topics anchor the course: Why Data Matters in AI; Collecting and Organizing Data; Cleaning and Preparing Data; and Ensuring Data Reliability and Ethics. This progression helps students connect foundational concepts, professional methods, analytical judgment, and applied decision-making in realistic data and AI-development environments.

Scenario-based exercises and a final applied task move the material from explanation to workplace use. Throughout the course, students practice distinguishing structured and unstructured data, planning data collection, identifying cleaning needs, evaluating bias and reliability, and applying FAIR data principles, producing a data-preparation and ethics analysis that documents data types, cleaning actions, quality risks, and privacy safeguards that can be discussed in exam-style questions and transferred to professional assignments.

KEY POINTS

  1. Why Data Matters in AI: Establishes the terminology, principles, and professional context needed to understand the topic’s significance and its connection to sound decisions.
  2. Collecting and Organizing Data: Examines practical workflows, tools, and standards that support consistent, efficient, and defensible execution.
  3. Cleaning and Preparing Data: Develops the ability to recognize risks or constraints, compare alternatives, and select appropriate controls, methods, or responses.
  4. Ensuring Data Reliability and Ethics: Connects course knowledge to planning, documentation, measurement, collaboration, and continuous improvement.
  5. Applied Practice: Uses scenarios and practical exercises to produce a data-preparation and ethics analysis that documents data types, cleaning actions, quality risks, and privacy safeguards and to justify decisions with evidence and accepted professional practice.
  6. Professional Standards and Readiness: Reinforces data quality, privacy, fairness, traceability, responsible governance, and clear analytical documentation for exam-style review and workplace application.

CORE LEARNING OUTCOMES

  1. Foundational Knowledge: Explain the principles, purpose, and professional significance of Topic 1, “Why Data Matters in AI,” within the collection, organization, preparation, quality, and ethical governance of data used by artificial intelligence systems.
  2. Methods and Tools: Apply structured methods, appropriate tools, and accepted practices to the work addressed in Topic 2, “Collecting and Organizing Data.”
  3. Analysis and Judgment: Analyze the conditions presented in Topic 3, “Cleaning and Preparing Data”; identify significant risks or opportunities; and select a defensible response.
  4. Solution Development: Develop and justify a practical approach to Topic 4, “Ensuring Data Reliability and Ethics.”
  5. Applied Deliverable: Produce a data-preparation and ethics analysis that documents data types, cleaning actions, quality risks, and privacy safeguards that demonstrates accurate, ethical, and well-documented application of course concepts.
  6. Professional Communication: Communicate findings and recommendations using terminology, documentation standards, and evidence appropriate to data and AI-development environments.
TSC1015 - Machine Learning Basics

COURSE DESCRIPTION

Machine Learning Basics introduces learners to the foundational concepts and practical logic that allow machine-learning systems to identify patterns and improve from data. Four connected topics anchor the course: What Is Machine Learning?; Supervised Learning; Unsupervised Learning; and Training, Testing, and Real-World Applications. This progression helps students connect foundational concepts, professional methods, analytical judgment, and applied decision-making in realistic machine-learning and decision-support environments.

Scenario-based exercises and a final applied task move the material from explanation to workplace use. Throughout the course, students practice explaining machine learning, comparing supervised and unsupervised methods, separating training and testing data, recognizing overfitting, and evaluating real-world use cases, producing a comparative machine-learning analysis that explains method selection, training and testing requirements, and model limitations that can be discussed in exam-style questions and transferred to professional assignments.

KEY POINTS

  1. What Is Machine Learning?: Establishes the terminology, principles, and professional context needed to understand the topic’s significance and its connection to sound decisions.
  2. Supervised Learning: Examines practical workflows, tools, and standards that support consistent, efficient, and defensible execution.
  3. Unsupervised Learning: Develops the ability to recognize risks or constraints, compare alternatives, and select appropriate controls, methods, or responses.
  4. Training, Testing, and Real-World Applications: Connects course knowledge to planning, documentation, measurement, collaboration, and continuous improvement.
  5. Applied Practice: Uses scenarios and practical exercises to produce a comparative machine-learning analysis that explains method selection, training and testing requirements, and model limitations and to justify decisions with evidence and accepted professional practice.
  6. Professional Standards and Readiness: Reinforces accurate terminology, appropriate method selection, validation, critical interpretation, and responsible use of model outputs for exam-style review and workplace application.

CORE LEARNING OUTCOMES

  1. Foundational Knowledge: Explain the principles, purpose, and professional significance of Topic 1, “What Is Machine Learning?,” within the foundational concepts and practical logic that allow machine-learning systems to identify patterns and improve from data.
  2. Methods and Tools: Apply structured methods, appropriate tools, and accepted practices to the work addressed in Topic 2, “Supervised Learning.”
  3. Analysis and Judgment: Analyze the conditions presented in Topic 3, “Unsupervised Learning”; identify significant risks or opportunities; and select a defensible response.
  4. Solution Development: Develop and justify a practical approach to Topic 4, “Training, Testing, and Real-World Applications.”
  5. Applied Deliverable: Produce a comparative machine-learning analysis that explains method selection, training and testing requirements, and model limitations that demonstrates accurate, ethical, and well-documented application of course concepts.
  6. Professional Communication: Communicate findings and recommendations using terminology, documentation standards, and evidence appropriate to machine-learning and decision-support environments.
TSC1020 - AI Tools and Applications

COURSE DESCRIPTION

AI Tools and Applications introduces learners to the selection, evaluation, and responsible workplace use of predictive, analytical, and generative artificial intelligence tools. Four connected topics anchor the course: Predictive Tools; Analytical Tools; Generative and Creative Tools; and Selecting and Using AI Tools Responsibly. This progression helps students connect foundational concepts, professional methods, analytical judgment, and applied decision-making in realistic business and technology environments.

Scenario-based exercises and a final applied task move the material from explanation to workplace use. Throughout the course, students practice comparing AI tool categories, matching tools to organizational needs, assessing benefits and limitations, identifying ethical risks, and planning responsible adoption, producing an AI-tool comparison and adoption recommendation supported by use-case, usability, oversight, privacy, and ethics criteria that can be discussed in exam-style questions and transferred to professional assignments.

KEY POINTS

  1. Predictive Tools: Establishes the terminology, principles, and professional context needed to understand the topic’s significance and its connection to sound decisions.
  2. Analytical Tools: Examines practical workflows, tools, and standards that support consistent, efficient, and defensible execution.
  3. Generative and Creative Tools: Develops the ability to recognize risks or constraints, compare alternatives, and select appropriate controls, methods, or responses.
  4. Selecting and Using AI Tools Responsibly: Connects course knowledge to planning, documentation, measurement, collaboration, and continuous improvement.
  5. Applied Practice: Uses scenarios and practical exercises to produce an AI-tool comparison and adoption recommendation supported by use-case, usability, oversight, privacy, and ethics criteria and to justify decisions with evidence and accepted professional practice.
  6. Professional Standards and Readiness: Reinforces transparent selection, human oversight, privacy protection, bias awareness, sound judgment, and responsible implementation for exam-style review and workplace application.

CORE LEARNING OUTCOMES

  1. Foundational Knowledge: Explain the principles, purpose, and professional significance of Topic 1, “Predictive Tools,” within the selection, evaluation, and responsible workplace use of predictive, analytical, and generative artificial intelligence tools.
  2. Methods and Tools: Apply structured methods, appropriate tools, and accepted practices to the work addressed in Topic 2, “Analytical Tools.”
  3. Analysis and Judgment: Analyze the conditions presented in Topic 3, “Generative and Creative Tools”; identify significant risks or opportunities; and select a defensible response.
  4. Solution Development: Develop and justify a practical approach to Topic 4, “Selecting and Using AI Tools Responsibly.”
  5. Applied Deliverable: Produce an AI-tool comparison and adoption recommendation supported by use-case, usability, oversight, privacy, and ethics criteria that demonstrates accurate, ethical, and well-documented application of course concepts.
  6. Professional Communication: Communicate findings and recommendations using terminology, documentation standards, and evidence appropriate to business and technology environments.