Master of Science and Doctor of Technology in Applied and Agentic AI

This course develops the computational thinking and algorithmic foundations required for intelligent systems. Learners study data structures, complexity, search, sorting, dynamic programming, graph algorithms, constraint satisfaction, symbolic reasoning, planning, and sequential decision-making. Classical algorithms are connected to contemporary applications such as retrieval, recommendation, constrained generation, routing, planning, and agent decision loops.

This course develops the core discipline of predictive machine learning. Learners frame business and scientific problems, prepare reliable datasets, build regression and classification models, engineer features, compare statistical and machine-learning approaches, and evaluate generalization. The course emphasizes leakage prevention, imbalance handling, calibration, uncertainty, bias-variance diagnosis, error analysis, and reliable model handoff.

This course extends machine learning into advanced representation, recommendation, temporal, probabilistic, causal, and sequential-decision systems. Learners apply clustering, dimensionality reduction, anomaly detection, recommender systems, ranking, time-series forecasting, causal reasoning, probabilistic modelling, and reinforcement-learning foundations to complex applied problems. The reinforcement-learning and decision-theoretic foundations developed here provide the basis for the alignment techniques covered in course TECH 501 Generative AI & LLM Engineering and the agentic decision-making covered in TECH 502 Agentic AI, Multi-Agent Systems and Orchestration.

This course develops learners’ ability to design, train, and evaluate advanced neural systems. It covers neural-network foundations, backpropagation, optimization, regularization, convolutional networks, sequence models, attention, transformers, representation learning, multimodal learning, graph neural networks, transfer learning, and efficient inference. Learners apply these architectures to vision, language, temporal, recommendation, multimodal, and connected data problems.

This course examines how contemporary foundation models and Generative AI systems are designed, trained, aligned, adapted, and evaluated. Learners study tokenization, embeddings, transformer internals, pre-training, scaling laws, mixture-of-experts architectures, context windows, instruction tuning, fine-tuning, PEFT, preference optimization, multimodal models, reasoning systems, and test-time compute. The emphasis is on understanding and engineering model behaviour rather than merely consuming hosted APIs.

This course develops learners’ ability to design reliable agentic and AI-native systems. It integrates agent harness design, planning, tool use, structured outputs, memory, reflection, workflow orchestration, human oversight, multi-agent collaboration, interoperability, non-deterministic testing, maintainability, and controlled autonomy. The emphasis is on framework-independent design and robust integration with enterprise systems. Retrieval and memory are addressed here as design patterns the agent reasons over; the underlying retrieval and memory infrastructure is engineered in TECH 503 AI Data Infrastructure: Pipelines, Retrieval & Knowledge Graphs.

This course develops the data infrastructure required for machine learning, deep learning, foundation models, retrieval systems, and agents. Learners design batch, distributed, streaming, feature, training data, retrieval, RAG, knowledge-graph, and memory pipelines. The course emphasizes data quality, provenance, labelling, synthetic data, contamination control, metadata, lineage, privacy, governance, and scalable knowledge access. Building on the agent memory and retrieval design patterns introduced in TECH 502 Agentic AI, Multi-Agent Systems and Orchestration, this course engineers the underlying retrieval, knowledge-graph, and memory systems that production agentic applications depend on.

This course develops the engineering capabilities required to train, deploy, scale, secure, monitor, and operate production AI systems. Learners study distributed training, GPU and accelerator systems, model serving, high-performance inference, containers, orchestration, MLOps, LLMOps, observability, reliability, security, cost engineering, capacity planning, and governance. The course treats AI as critical infrastructure rather than a standalone software feature.

This course provides significant emerging areas of AI that do not yet warrant dedicated core courses or that extend core program content into specialized domains. Because the AI field evolves rapidly, the topics offered are reviewed and refreshed regularly to reflect current research, industry, and policy developments. Learners engage with one or more thematic modules selected from a rotating set of offerings — spanning emerging technical paradigms (e.g., sovereign AI, edge AI/TinyML, causal AI, federated learning, quantum AI) and responsible AI governance (e.g., regulation, algorithmic auditing, and responsible scaling).

The capstone course requires learners to design, build, train or adapt, evaluate, deploy, scale, secure, and defend a complete AI system. Projects may involve predictive machine learning, recommender systems, deep learning, foundation models, multimodal AI, retrieval, agents, or AI infrastructure. A submission based only on calling an external API, creating prompts, or presenting a conceptual architecture is not sufficient.

This course bridges the transition from graduate level competency to doctoral-level inquiry. Learners develop doctoral reading, critical synthesis, and scholarly communication skills through structured engagement with frontier AI research, including applied AI, generative AI, agentic systems, and responsible AI governance. They identify a significant doctoral problem, justify a knowledge or practice gap, design a contribution framework, align an evidence and evaluation strategy, and address ethical, governance, security, and impact considerations. The course culminates in a doctoral seminar that provides a structured forum for presenting, defending, and refining research ideas before peers and faculty, building the presentation and scholarly argumentation skills required for doctoral candidacy, conference participation, and dissertation defense.

After the foundation and advanced graduate curriculum coursework or after completion of the MS in Applied and Agentic AI, students must successfully pass a qualifying examination prior to proceeding to the dissertation phase. TECH 810 is the integrative qualifying examination that tests the student’s mastery of the skills and disciplines of doctoral-level research methods and analysis acquired across the complete foundation and advanced curriculum sequence.

This course introduces doctoral-level research design, qualitative and quantitative methods, mixed methods, literature synthesis, data analysis, and research ethics. The course focuses on applying research methods to investigate and solve real-world problems of practice in AI, technology, and organizational contexts. Learners develop the ability to evaluate research designs, select appropriate methods, analyze data responsibly, and connect research evidence with practical technology implementation. The course also prepares learners to develop a rigorous and defensible dissertation proposal.

This course develops doctoral-level competency in both qualitative and quantitative analysis methods as applied to research in the AI domain, equipping learners with the analytical toolkit required to investigate significant problems of applied and agentic AI practice. Building on the research design foundations of TECH 805, learners develop proficiency in selecting, applying, and critically evaluating analysis methods appropriate to their doctoral research question and contribution type — spanning quantitative methods (statistical inference, effect size, uncertainty quantification, benchmark analysis, reproducibility validation), qualitative methods (thematic analysis, grounded theory, case study, expert interview, discourse analysis), mixed-method designs, and AI-specific evaluation approaches (LLM-as-judge, human evaluation, automated metrics, red-teaming). The course emphasizes methodological fit, analytical validity, and the responsible and defensible interpretation of results in the context of doctoral AI research.

This course guides learners through the development of a formal Dissertation Topic Proposal. Learners refine the research direction established in TECH 809 into a fully developed, academically defensible proposal that establishes the precise research problem and its significance, synthesises the relevant literature and identifies the research gap, defines research questions or objectives, articulates the proposed methodology, and provides an initial ethical, responsible AI, and governance assessment. The topic proposal demonstrates that the intended dissertation is relevant, feasible, ethical, and grounded in both professional practice and applied scholarship — and establishes the foundation for moving toward the full Dissertation Proposal Defense in TECH 891.

Learners expand the Dissertation Topic Proposal approved in TECH 890 into a comprehensive Dissertation in Practice Proposal covering the research problem, complete literature foundation and theoretical framework, full methodology design, data sources, ethical governance, analytical approach, and expected contribution to practice. The defense emphasizes feasibility, defensibility, methodological rigor, and practical impact. Learners are expected to demonstrate that their proposed research is sufficiently developed, appropriately scoped, and capable of producing meaningful insights, interventions, or recommendations for real-world AI and technology practice. Successful defense grants doctoral candidacy. Prerequisite(s): TECH 890.

Students execute and complete their doctoral research as a Dissertation in Practice. This stage involves conducting the approved study, analysing findings using the approved analytical approach, developing or evaluating an applied solution or improvement pathway, and communicating the practical and scholarly implications in a completed Dissertation in Practice document. The final dissertation submission and public defense demonstrate the learner’s ability to investigate a real-world problem of practice, use appropriate research methods, generate evidence-based insights, and contribute to applied AI or technology practice at doctoral level.