From Algorithms to AI: Building a Modern Machine Learning Curriculum

Author: Denis Avetisyan

A new course design effectively integrates the fundamentals of traditional machine learning with the latest advancements in large language models to prepare students for the future of artificial intelligence.

This paper details a two-part curriculum focusing on prompt engineering, RAG, and model fine-tuning to bridge the gap between classic techniques and contemporary LLMs.

Despite the rapid advancement of Large Language Models, a robust understanding of foundational machine learning principles remains crucial for navigating the evolving landscape of artificial intelligence. This paper details an innovative pedagogical approach, ‘Bridging Traditional Machine Learning and Large Language Models: A Two-Part Course Design for Modern AI Education’, presenting a curriculum structured to systematically integrate these complementary skillsets. Our findings demonstrate that this two-part design-covering both established techniques and cutting-edge LLM applications-significantly enhances student comprehension and practical ability. Will this integrated approach become a standard for preparing the next generation of AI professionals for industry demands?

The Foundations of Understanding: Building Beyond Traditional Machine Learning

The emergence of large language models (LLMs) doesn’t negate the importance of traditional machine learning; rather, it builds directly upon its foundations. A robust understanding of statistical principles and data analysis techniques, historically central to machine learning, remains critical for effectively working with LLMs. Before the advent of deep learning, techniques like regression, classification, and clustering formed the bedrock of predictive modeling. These methods necessitate careful data preprocessing, feature engineering – the process of selecting and transforming variables – and model evaluation using metrics like precision and recall. This analytical groundwork isn’t simply historical; it provides the essential framework for understanding how LLMs learn patterns from data, assess their performance, and mitigate biases. Indeed, the ability to critically examine the data used to train LLMs, a skill honed through traditional machine learning, is paramount to ensuring their responsible and effective application.

The progression toward sophisticated machine learning models isn’t a leap, but a carefully constructed ascent built upon foundational concepts like feature engineering and neural networks. Feature engineering, the process of selecting, transforming, and creating relevant input variables, allows algorithms to discern meaningful patterns within raw data; a well-engineered feature set dramatically improves model accuracy. This groundwork then paves the way for understanding neural networks – interconnected layers of nodes inspired by the human brain – which learn increasingly complex representations from these features. These networks, initially simple in structure, form the basis for deep learning architectures and ultimately, large language models. The ability to manipulate and interpret data through feature engineering, combined with the representational power of neural networks, isn’t merely historical context; it’s a prerequisite for comprehending how more advanced models function and why certain architectures excel, providing the essential toolkit for innovation in the field.

Successfully navigating the landscape of large language models necessitates a firm grounding in foundational machine learning principles. While LLMs appear as sophisticated, almost magical systems, they are built upon established concepts like feature engineering, model evaluation, and the architecture of neural networks. Attempting to understand or modify these complex models without appreciating these underpinnings is akin to attempting surgery without anatomical knowledge. A robust understanding of these core concepts allows for effective troubleshooting, informed experimentation, and ultimately, the ability to harness the full potential of LLMs – moving beyond simply using the technology to truly understanding and advancing it. Without this base, practitioners risk being limited to superficial adjustments and unable to address the nuances inherent in these powerful systems.

From Theory to Practice: LLMs and the Constraints of Computation

Large Language Models (LLMs) demonstrate a substantial advancement in Natural Language Processing (NLP) through their capacity to perform intricate tasks such as text generation, translation, and question answering with a degree of fluency previously unattainable. This capability is achieved via deep neural networks, often utilizing the transformer architecture, trained on massive datasets – frequently exceeding hundreds of gigabytes – and containing billions of parameters. However, the performance of LLMs is directly correlated with computational resources; training these models necessitates significant processing power, typically requiring specialized hardware like GPUs or TPUs, and considerable energy consumption. Inference, while less demanding than training, still requires substantial resources, particularly for real-time applications and longer input sequences, creating barriers to accessibility and widespread deployment.

This course utilizes computationally efficient Large Language Models (LLMs) – specifically DistilGPT-2, Phi-2, and LLaMA-3.1 – to facilitate practical experience with minimal hardware demands. DistilGPT-2 is a distilled version of GPT-2, reducing the number of parameters while retaining a significant portion of its performance. Phi-2, developed by Microsoft, is a 2.7 billion parameter model demonstrating strong reasoning capabilities despite its relatively small size. LLaMA-3.1, a member of the LLaMA family, offers an open-weight option prioritizing accessibility and performance. By focusing on these models, the course circumvents the need for expensive and specialized hardware typically required to train and deploy larger LLMs, making hands-on learning more accessible to a wider audience.

Fine-tuning pre-trained Large Language Models (LLMs) involves updating the model’s weights using a smaller, application-specific dataset. This process leverages the knowledge already encoded within the LLM – acquired during its initial training on massive datasets – and adapts it to perform more effectively on a target task. Techniques include full parameter fine-tuning, where all model weights are updated, and parameter-efficient fine-tuning (PEFT) methods like LoRA and adapters, which modify only a small subset of parameters, reducing computational cost and storage requirements. The selection of appropriate hyperparameters, such as learning rate, batch size, and number of epochs, is crucial for successful fine-tuning and preventing overfitting to the new dataset. Evaluation metrics relevant to the specific application are then used to assess the performance of the fine-tuned model.

The Art of Prompting and the Power of Retrieval

Prompt engineering involves designing effective input queries, known as prompts, to elicit specific and desired responses from Large Language Models (LLMs). The performance of an LLM is heavily influenced by prompt structure, clarity, and the inclusion of relevant context or instructions. Techniques include specifying the desired format of the output, providing examples of input-output pairs (few-shot learning), and utilizing chain-of-thought prompting to encourage the model to articulate its reasoning process. Iterative refinement of prompts, based on observed outputs, is crucial for optimizing LLM performance for a given task. Poorly constructed prompts can lead to ambiguous, irrelevant, or inaccurate responses, highlighting the need for skilled prompt design as a core competency in utilizing LLMs effectively.

Retrieval-Augmented Generation (RAG) enhances Large Language Model (LLM) performance by integrating external knowledge sources into the generation process. Instead of relying solely on parameters learned during training, RAG systems first retrieve relevant documents or data from a knowledge base – which can include databases, files, or APIs – based on the user’s query. This retrieved information is then incorporated into the prompt provided to the LLM, providing additional context and factual grounding. By augmenting the LLM’s internal knowledge with external data, RAG improves the accuracy, relevance, and specificity of generated responses, particularly for queries requiring up-to-date or domain-specific information not present in the LLM’s original training data.

Vector databases are crucial components of Retrieval-Augmented Generation (RAG) systems, facilitating efficient knowledge retrieval by representing data as high-dimensional vectors. These databases utilize algorithms, such as those implemented in the FAISS (Facebook AI Similarity Search) library, to perform approximate nearest neighbor searches. Instead of matching keywords, similarity search identifies vectors that are close to each other in vector space, indicating semantic similarity. This enables RAG systems to quickly locate relevant information from large datasets based on the meaning of a query, rather than exact textual matches, and significantly improves the accuracy and context of LLM outputs. The efficiency of FAISS and similar tools is achieved through indexing techniques that trade off some accuracy for substantial speed gains in high-dimensional space.

From Development to Deployment: Bridging the Gap to Real-World Impact

Successfully implementing Large Language Models (LLMs) extends beyond initial development and requires a crucial step: deployment, which focuses on making these powerful tools readily available to end-users. However, the substantial computational demands of LLMs often present a significant hurdle to widespread accessibility. To address this, techniques like quantization are frequently employed; this process reduces the precision of the model’s parameters – for example, from 32-bit floating point numbers to 8-bit integers – thereby dramatically decreasing both memory usage and computational costs. While some degree of accuracy may be sacrificed, quantization allows LLMs to run efficiently on less powerful hardware, opening doors for integration into a wider range of applications and devices, and ultimately enabling broader access to this transformative technology.

The development and deployment of large language model (LLM) applications are significantly streamlined through integrated frameworks like the Hugging Face Ecosystem and LangChain. Hugging Face provides a vast repository of pre-trained models and tools for fine-tuning and evaluation, while LangChain acts as an orchestration layer, enabling developers to connect LLMs to various data sources and build complex, multi-step applications. This synergy allows for rapid prototyping and deployment of solutions ranging from chatbots and text summarization tools to sophisticated question answering systems and code generation platforms. By abstracting away much of the underlying complexity, these tools empower developers to focus on application logic and user experience, accelerating innovation in the field of artificial intelligence and making LLM technology more accessible than ever before.

The evaluation strategy for this course prioritizes the demonstration of applied skills. A significant 80% of the final grade is directly tied to practical work, split evenly between individual assignments and collaborative group projects, fostering both independent learning and teamwork. The remaining 20% acknowledges the importance of engagement and mutual support through active participation and a peer collaboration evaluation, ensuring students not only master the concepts but also contribute to a dynamic learning environment and benefit from shared knowledge.

Empowering Agents: The Future of Contextual Integration

The Model Context Protocol represents a significant advancement in artificial intelligence, allowing agents to move beyond simple language processing and actively utilize external tools to solve complex problems. This protocol doesn’t merely instruct an agent what to do, but provides the necessary contextual information for the agent to understand how to employ specific tools-be it a search engine, a calculator, or a specialized API-to achieve a defined goal. By effectively mediating between the agent’s reasoning and the functionalities of these tools, the protocol fosters a dynamic problem-solving process where agents can independently gather information, perform calculations, and execute actions, dramatically expanding their capabilities and paving the way for more versatile and intelligent AI systems.

The development of truly adaptable and intelligent artificial intelligence hinges on seamless integration between large language models and external tools – a process that moves beyond simple task completion towards genuine problem-solving. Without this crucial interplay, AI remains limited to the data it was initially trained on, unable to leverage the ever-expanding wealth of information and functionalities available in the real world. This integration isn’t merely about accessing tools; it’s about the AI’s capacity to understand when and how to utilize them, to chain together different functionalities, and to learn from the outcomes – effectively building systems that can navigate complex scenarios and respond to novel challenges with increasing sophistication. Consequently, fostering this interconnectedness is paramount to unlocking the full potential of AI and building agents capable of dynamic, context-aware reasoning.

This curriculum serves as a critical stepping stone for individuals seeking to harness the full potential of large language models and autonomous agent technologies. By establishing a firm grasp of foundational principles, participants gain the necessary skillset to move beyond basic implementations and delve into sophisticated applications. The course isn’t merely about understanding how these technologies function, but rather about equipping learners with the tools to innovate-to design, build, and deploy intelligent agents capable of tackling increasingly complex real-world challenges. Through a focused exploration of core concepts, the program prepares individuals to confidently navigate the rapidly evolving landscape of AI and contribute to the development of next-generation intelligent systems, fostering a deeper understanding of both the capabilities and limitations of these powerful tools.

The pursuit of a curriculum that cohesively integrates established machine learning principles with the emergent capabilities of Large Language Models reveals a fundamental truth about complex systems. This course design isn’t about imposing order, but about cultivating an environment where both foundational knowledge and novel techniques can coexist and evolve. As Barbara Liskov observed, “Programs must be correct, but they also must be understandable.” This sentiment applies directly to the intention of bridging traditional methods with LLMs; a student must not only grasp how a model functions, but also why it functions, ensuring comprehension isn’t sacrificed at the altar of innovation. A system that neglects its roots is destined for brittle failure, and this curriculum recognizes that sustainable growth demands a solid foundation.

The Looming Silhouette

This curriculum, intended as a bridge, inevitably reveals the widening gulf it spans. The effort to sequence foundational machine learning before immersion in Large Language Models presumes a linearity that will not hold. Each algorithm taught becomes a quaint artifact, a relic of a time when explicit optimization held sway. The true lesson, though unstated, is not what these models are, but what they conceal – a shift from engineering to gardening, from control to guided chaos.

The emphasis on RAG and fine-tuning, while practical, skirts the central problem. These are techniques for managing the opacity, not for penetrating it. Future iterations of this course will not be judged on how well students can prompt a model, but on their ability to diagnose the inevitability of its failures. The next challenge isn’t building better tools, but cultivating a tolerance for unpredictable outcomes-a curriculum in graceful degradation.

Consider the lifecycle of any pedagogical pattern. This attempt to codify a skillset will, within a single release cycle, become a case study in obsolescence. The architecture of knowledge itself is shifting, and this course, like all others, is merely a temporary bulwark against the rising tide of unknowability. The real work begins when the syllabus is abandoned.

Original article: https://arxiv.org/pdf/2512.05167.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/