Practical Machine Learning Course Project at the University of Bucharest: Balancing Academic Training and Engineering Practice

This article introduces the Practical Machine Learning (PML) course project at the University of Bucharest, exploring how to balance academic theoretical depth and engineering practice capabilities to cultivate AI talents who understand algorithm principles and can solve real-world problems. As a core master's course, it emphasizes theoretical foundation, engineering practice, and problem-solving skills. Through training in complete project cycles and application of real data, it provides a fault-tolerant learning environment and peer collaboration opportunities, serving as a reference example for AI education that balances theory and practice.

The development of artificial intelligence poses dual challenges to education: theory is becoming increasingly complex (from classical statistics to deep learning), while the industry emphasizes practical skills more (data cleaning, model deployment, etc.). Traditional education leans toward theory, leaving students at a loss when facing real data; pure engineering training camps allow quick mastery but lack deep understanding. Excellent AI education needs to balance the two, and the PML course at the University of Bucharest embodies this concept.

As a core course in the first semester of the AI master's program at the Faculty of Mathematics and Computer Science of the University of Bucharest, the PML course has clear positioning: 1) Theoretical foundation: Understand the mathematical principles of classical ML algorithms; 2) Engineering practice: Master the complete workflow from data to deployment; 3) Problem-solving: Cultivate the ability to adapt methods to tasks. The course emphasizes mathematical rigor and practical application, and is committed to cultivating meta-competencies that adapt to technological evolution.

The theoretical module starts with statistical learning theory: supervised learning (empirical/structural risk minimization, overfitting trade-off), generalization theory (PAC framework, VC dimension, etc. to quantify generalization ability), and optimization basics (gradient descent and convergence analysis). It also focuses on classical algorithms: linear models, support vector machines (kernel tricks), decision trees and ensembles (random forests, gradient boosting), clustering and dimensionality reduction (K-means, PCA, etc.), forming a learning toolbox to help make informed decisions in method selection.

The practical module requires students to go through the complete machine learning life cycle: problem definition → data exploration → preprocessing and feature engineering → model selection and training → result analysis → report presentation. Using real datasets from Kaggle/UCI, evaluation not only looks at accuracy but also requires reporting precision-recall curves, confusion matrices, etc. It also emphasizes code quality: Git version control, documentation, reproducibility, and modularity, making up for the lack of engineering practice in academia.

The course provides a fault-tolerant learning environment that allows trying failed methods; open-source project code promotes peer learning and feedback; completed projects can be used as job-seeking portfolios, which prove practical ability better than transcripts. These features distinguish it from industrial projects and traditional closed assignments, cultivating collaboration and practical awareness.

The course needs optimization: 1) Theoretical depth: Due to limited time in one semester, deep learning coverage is shallow; class hours can be adjusted or a special follow-up course can be set up; 2) Engineering practice: Add content such as model deployment, production monitoring, and A/B testing; 3) Ethical impact: Introduce case discussions on algorithm bias, privacy risks, etc., to cultivate awareness of technical ethics.

Enlightenment for educators: 1) Bidirectional reinforcement of theory and practice (theory guides practice, practice deepens theory); 2) Integrate open-source culture to cultivate community participation awareness; 3) Comprehensive evaluation (code quality, documentation, analysis depth, etc.) to transmit the values of "good engineering". The course concept provides a reference for AI education and helps build effective learning paths.