Core Viewpoint - The article discusses the importance of foundational knowledge in machine learning education, emphasizing that understanding core algorithms and mathematical principles is crucial for long-term success in the field, especially in the context of rapidly evolving technologies like LLMs [2][20][23]. Group 1: Course Design and Focus - The machine learning course designed by Kyunghyun Cho for the 2025 academic year focuses on foundational algorithms like Stochastic Gradient Descent (SGD) while intentionally avoiding large language models (LLMs) [2][7]. - Other prestigious institutions, such as Stanford and MIT, also emphasize foundational theories and classic models in their machine learning curricula, indicating a broader trend in academia [2][4]. - The course encourages students to study classic papers to understand the historical development of machine learning theories, which is seen as beneficial for critical thinking [7][23]. Group 2: Theory vs. Practice - There is a tension between the academic focus on foundational principles and the practical skills required in industry, where rapid deployment and iteration are often prioritized [9][20]. - Some universities are addressing this gap by offering bridge courses or practical projects, such as Stanford's CS329S, which focuses on building deployable machine learning systems [9][11]. - CMU's machine learning doctoral program includes a practical course where students must build and deploy a complete machine learning system, highlighting the importance of hands-on experience [11][13]. Group 3: Importance of Foundational Knowledge - The article argues that a strong foundation in machine learning is essential for adapting to new technologies and for fostering innovation in research [17][20][23]. - Geoffrey Hinton emphasizes that the breakthroughs in deep learning were built on decades of foundational research, underscoring the value of understanding core algorithms [23]. - The article posits that practical skills should be built upon a solid understanding of underlying principles, suggesting that foundational knowledge is a long-term asset in the tech industry [20][23]. Group 4: Course Content Overview - The course syllabus includes comprehensive topics such as energy functions, basic classification algorithms, neural network components, and probabilistic machine learning [26]. - Advanced topics covered in the course include reinforcement learning, ensemble methods, and Bayesian machine learning, indicating a thorough approach to machine learning education [27]. Group 5: Classic Papers and Their Impact - The article references several classic papers that have significantly influenced machine learning, such as the REINFORCE algorithm and the introduction of Variational Autoencoders (VAEs) [30][32][34]. - These foundational works are crucial for understanding modern machine learning techniques and their applications [30][32].