Subject Overview

This subject provides a structured foundation in artificial intelligence and machine learning, combining classical AI, data-driven learning, modern generative AI, professional implementation, managerial decision-making, and responsible governance.

Learning Goal

Students will be able to explain essential AI and machine learning concepts, formulate suitable problems, prepare data, select and evaluate models, interpret results, develop basic AI solutions, support organizational adoption, and apply ethical and governance principles.

Syllabus

1. Foundations of Artificial Intelligence and Machine Learning

Learning Objective: Define AI, machine learning, deep learning, data science, and generative AI; trace their evolution; distinguish symbolic, statistical, and hybrid approaches; and identify major capabilities, limitations, and applications.

Category: foundation

2. Mathematical and Statistical Foundations

Learning Objective: Apply essential concepts from linear algebra, probability, descriptive statistics, distributions, calculus, optimization, correlation, and statistical inference to understand machine learning models.

Category: foundation

3. Intelligent Agents, Reasoning, and Search

Learning Objective: Explain rational agents, environments, knowledge representation, logic, inference, state-space search, heuristics, constraint satisfaction, planning, and decision-making under uncertainty.

Category: core

4. Data Foundations and the AI Data Lifecycle

Learning Objective: Understand data types, sources, collection, labeling, quality, storage, integration, privacy, bias, feature representation, and lifecycle management for reliable AI systems.

Category: core

5. Machine Learning Workflow and Toolchain

Learning Objective: Translate business problems into learning tasks and perform data exploration, preprocessing, feature engineering, model selection, training, validation, documentation, and reproducible experimentation using contemporary tools.

Category: core

6. Supervised Learning

Learning Objective: Develop conceptual and practical understanding of regression and classification using linear and logistic regression, decision trees, ensembles, support vector machines, nearest-neighbor methods, and probabilistic classifiers.

Category: core

7. Unsupervised Learning and Representation Discovery

Learning Objective: Apply clustering, dimensionality reduction, association analysis, anomaly detection, and latent representation techniques to discover patterns in unlabeled data.

Category: core

8. Neural Networks and Deep Learning

Learning Objective: Explain perceptrons, multilayer networks, activation functions, loss functions, backpropagation, gradient-based optimization, regularization, convolutional networks, recurrent models, and transformers.

Category: advanced

9. Domain AI and Generative Models

Learning Objective: Examine natural language processing, computer vision, speech systems, recommender systems, foundation models, large language models, embeddings, prompt engineering, retrieval-augmented generation, and multimodal AI.

Category: contemporary

10. Model Evaluation, Interpretation, and Decision Analytics

Learning Objective: Select appropriate metrics; use cross-validation, baselines, error analysis, calibration, explainability, uncertainty estimation, and cost-sensitive evaluation; and convert model outputs into defensible decisions.

Category: application

11. AI Solution Development and MLOps

Learning Objective: Build and deploy basic AI pipelines using APIs, cloud or edge platforms, versioning, containers, automated testing, model registries, monitoring, drift detection, retraining, security, and lifecycle controls.

Category: application

12. Business Applications and Digital Transformation

Learning Objective: Evaluate AI use cases across organizational functions, assess feasibility and value, redesign workflows, support human–AI collaboration, measure return on investment, manage change, and align AI initiatives with digital strategy.

Category: strategic

13. Responsible AI, Ethics, Risk, and Governance

Learning Objective: Address fairness, transparency, accountability, privacy, safety, intellectual property, cybersecurity, human oversight, regulatory compliance, model risk, auditability, and responsible AI governance frameworks.

Category: strategic

14. Industry Practice, Emerging Trends, and Future Directions

Learning Objective: Assess current industry requirements and emerging developments including AI agents, small and domain models, synthetic data, AutoML, federated learning, causal AI, neuro-symbolic systems, edge AI, sustainable AI, and evolving careers and regulation.

Category: contemporary