updated description

Author: DannyW618

index.html

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                 <p class="university-brand">The University of Queensland</p>
                 <h1>Advanced Techniques for High Dimensional Data <span class="course-code">(INFS4205/7205)</span></h1>
                 <p
-                    style="font-size: 1.125rem; max-width: 48rem; margin: 1.5rem auto 0; line-height: 1.7; text-align: justify;">
-                    Selected advanced topics from spatial and multimedia databases: multidimensional data management
+                    style="font-size: 1.0rem; max-width: 52rem; margin: 1.5rem auto 0; line-height: 1.7; text-align: justify;">
+                    <!-- Selected advanced topics from spatial and multimedia databases: multidimensional data management
                     concepts, theories and technologies, focusing on data access methods and similarity query
                     processing for spatial, multimedia and Web-based databases, with particular emphasis on
-                    video indexing and search.
+                    video indexing and search. -->
+                    This course explores how modern AI systems represent, align, retrieve, and reason over
+                    high-dimensional multimodal data. Contemporary AI systems, including multimodal foundation models,
+                    retrieval-augmented generation
+                    (RAG), and agents, rely on representing text, images, and other modalities as high-dimensional
+                    embeddings. Understanding how these representations are learned, aligned, indexed, and used for
+                    reasoning is essential for building reliable AI applications.
+                    <br>
+                    <br>
+                    The course takes a systems view of multimodal AI: from representation learning and alignment to
+                    vector databases and scalable multimodal RAG, followed by efficiency techniques and agent
+                    architectures that enable interactive AI systems. It concludes with frontier applications and
+                    personalization methods such as parameter-efficient fine-tuning.
+                    A central theme is vibe coding: designing AI-assisted systems by reasoning about intent, structure,
+                    and evaluation rather than syntax alone. Through reflective inquiry and a hands-on multimodal
+                    chatbot project, students learn to build, analyze, and critically evaluate modern AI pipelines.
+                    <!-- By the end of the course, students will understand how high-dimensional representations underpin
+                    multimodal AI and gain practical experience constructing retrieval-augmented and agent-based
+                    systems. -->
                 </p>
             </div>