Artificial Intelligence @UPLB

In this course, we explore what it means to make machines think, learn, and act intelligently. Together, we’ll study the foundations of AI through a balance of theory and hands-on experience—learning how intelligent behavior can emerge from computation, data, and real-world interaction.

The course is structured around three major parts that reflect how intelligence manifests across different dimensions of computing:

1. Intelligence from Computation – How reasoning, problem-solving, and search algorithms form the computational core of intelligent systems.

2. Intelligence from Data – How learning from data enables machines to recognize patterns, make predictions, and adapt to changing environments.

3. Intelligence in the Wild – How AI interacts with the real world through perception, communication, and action in complex, dynamic settings.

These three parts together form a holistic view of Artificial Intelligence—one that not only examines how machines can be intelligent, but also why intelligence matters in human and social contexts. The specific topics discussed in these three major parts are presented in the Topics page.

To help students navigate the evolving course structure and instructional approaches, previous Course Guides from past academic years and semesters are also made available below. Each guide reflects how the course has grown with emerging trends in AI education and research, offering insight into both its history and its ongoing development.

• AY 2021-2022: 2nd Semester*

• AY 2022-2023: 1st Semester* & 2nd Semester*

• AY 2023-2024: 1st Semester*

• AY 2024-2025: 1st Semester*

• AY 2025-2026: 1st Semester* (current)

Note: Links marked with an asterisk (*) lead to materials accessible only to members of the University community. Please log in with your official University account to view them.

At CMSC, we offer a range of specialized courses designed to deepen your understanding of Artificial Intelligence and expand your technical expertise. These courses extend AI into various cutting-edge applications, including robotics, neural computing, natural language processing, and unmanned aerial vehicles. Some courses focus on AI systems themselves, while others provide critical supporting knowledge, from parallel computing to big data and cybersecurity.

Courses That Directly Extend AI:

• CMSC 172: Robot Modeling – Explore the foundations of robotic systems, including modeling, control, and simulation techniques for building autonomous machines.

• CMSC 191: Special Topics (Introduction to Neural Computing) – Delve into neural networks and deep learning techniques, understanding how the brain-inspired algorithms drive modern AI.

• CMSC 191: Special Topics (Generative AI) – Study the revolutionary field of generative models, including deep generative networks, and their impact on creative AI applications.

• CMSC 191: Special Topics (Introduction to Natural Language Processing) – Learn how machines understand and generate human language through AI-driven techniques like sentiment analysis and machine translation.

• CMSC 191: Special Topics (Advanced Unmanned Aerial Vehicle Modeling) – Dive into AI applications in autonomous flight, focusing on the modeling, simulation, and control of unmanned aerial vehicles (UAVs).

• CMSC 291: Special Topics (Advanced Neural Computing) – A graduate-level course that takes a deeper dive into advanced neural network architectures and cutting-edge computational models.

Courses That Support AI Systems:

• CMSC 180 - Introduction to Parallel Computing – Understand the principles of parallel computation and how they optimize AI system performance, from large-scale data processing to real-time analytics.

• CMSC 191 - Special Topics (Big Data: Management and Trends) – Learn the essential skills for managing and processing large datasets—key for training robust AI models.

• CMSC 191 - Special Topics (Knowledge Management Systems) – Study the systems that help store, manage, and analyze knowledge—vital for developing intelligent systems capable of reasoning and decision-making.

• CMSC 191 - Special Topics (Cybersecurity) – Explore AI's role in cybersecurity, from threat detection to securing AI systems against vulnerabilities.

• CMSC 280 - Parallel Processing (graduate level) – This course covers advanced techniques in parallel programming, essential for maximizing the performance of AI applications, especially those requiring large-scale computations.

Note: Special Topics courses are initiated, designed, taught, and managed by the faculty as an expression of academic freedom and as a means of keeping pace with emerging global trends in the computing discipline.

Artificial Intelligence continues to evolve, and so does our curriculum. As part of our commitment to keep teaching at the frontier of innovation, several Special Topics courses that once captured students’ interest and proven their academic value are now being proposed to become part of the regular CMSC program. These proposed courses represent the next wave of AI learning—bridging foundational theory with real-world application, and ensuring our students remain globally competitive in the era of intelligent systems.

Proposed Courses for Institutional Offering:

• AI 1: AI in Nation Building and Everyday Living – Explores the impact of artificial intelligence on society, governance, and daily life, with emphasis on the Filipino context. When approved, this course will form part of UPLB's General Education Program, allowing students from all disciplines to appreciate AI’s role in nation building.

• CMSC 171: Fundamentals of Machine Learning – A comprehensive introduction to the principles and algorithms that allow computers to learn from data. This course explores supervised and unsupervised learning, model evaluation, and practical implementation techniques that form the backbone of modern AI.

• CMSC 175: Introduction to Natural Language Processing – Building upon earlier explorations of language-aware AI systems, this course covers the fundamental methods for processing, understanding, and generating human language, preparing students to build intelligent systems that can truly “communicate.”

• CMSC 177: Generative AI Models – A deep dive into the models that create—text, images, sound, and beyond. Students will explore architectures such as GANs, VAEs, and diffusion models, understanding how generative AI is reshaping creativity, research, and industry.

• CMSC 179: Fundamentals of Neural Computing – Designed as an essential continuation of AI foundations, this course examines artificial neural networks and their biological inspirations, emphasizing both theoretical insights and hands-on applications across various domains.

Note: Most courses proposed for institutional offering are those that began as Special Topics courses—initiated, designed, taught, and managed by the faculty under academic freedom—and later earned their place as regular courses in the program due to their demonstrated popularity, relevance, and usefulness among students.