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Katalog Mata Kuliah GHC 14 Mata Kuliah

Klik pada kartu mata kuliah untuk melihat jadwal, prasyarat, dan detail silabus lengkap.

Computer Science and Artificial Intelligence

This full – English course covers AI fundamentals, history, computational foundations, data foundations, hardware foundations, machine learning, deep learning, large models, intelligent agents, and the latest autonomous agents such as OpenClaw. It enables students to systematically grasp the theoretical framework, recent developments, and ethical considerations of AI.

Data Structures and Algorithms Design

Beijing Institute of Technology's "Data Structures and Algorithms Design (full – English)" is a foundational course for information majors outside computer science. It offers comprehensive coverage of classical data structures and core algorithms, with a strong focus on complexity analysis and performance evaluation. The course features an innovative integration of AIGC tools across all teaching stages, guiding students to develop critical thinking in human – AI collaboration. All practical exercises are implemented in C++, effectively enhancing students' engineering practice capabilities.

Scientometrics and Data Analytics

This full – English core course for the national first – class undergraduate program "Information Management and Information Systems" cultivates interdisciplinary talent in digital technology and digital management, aiming to develop digitally innovative talents with a global perspective. Through case – based teaching and project – based learning, it integrates information system analysis and design methods into digital management scenarios, effectively stimulating student interest and potential. AI – assisted instruction enriches classroom interaction and delivers significant learning outcomes.

Artificial Intelligence：Fundamentals and Frontiers

The School of Electronics and Information at Northwestern Polytechnical University has conducted AI research and education since the early 1990s. This full – English course focuses on cutting – edge areas such as spatial intelligence and embodied intelligence, balancing foundational theory with international frontiers. It bridges graduate students' foundational AI knowledge with the university's distinctive research strengths, continuously supporting interdisciplinary AI talent development and original innovation.

Algorithm Design and Analysis

Core course for Computer Science and Technology.

Theory of Machine Learning

This course is a core theoretical course for graduate students in intelligent disciplines. Distinct from engineering – focused machine learning courses, it emphasizes underlying mathematical theory and generalization performance analysis. Leveraging the school's strengths in neuromorphic computing, intelligent recognition, and marine intelligent equipment, it builds a rigorous theoretical analysis framework.

Introduction to High Performance Computing

A core elective for graduate students, featuring scenario – based teaching using HEU's "Three Seas and One Nuclear" strengths in ship simulation, underwater acoustic big data, and marine intelligent simulation.

Computational Thinking

Core Computer Science Courses

Intelligent Control

Automation in the AI era, covering cutting – edge control theory. A long – standing bilingual course with a strong foundation in English instruction.

Digital Speech Signal Processing

A required course that applies digital signal processing to analyze speech and audio, forming the foundation for speech recognition, synthesis, and other voice interaction applications.

Cyber – Physical Systems Safety and Security

Cyber – physical systems are the core of critical information infrastructure, and their security is paramount.

Advanced System Modeling and Simulation

A foundational theoretical methods course for the digital and intelligent era.

Academic English Writing

This course is designed for top – tier undergraduate researchers to meet their scientific research training and international academic communication needs. It addresses key challenges in student research writing – unclear expression, structural issues, and underemphasized research value – strengthening their ability to transform research into international academic output. Leveraging the Rain Classroom AI platform, the course has built a knowledge base, course models, AI application systems, and teaching data analytics, integrating AI support throughout pre – class preparation, in – class training, post – class revision, and research writing. Two AI agents – "Reviewer Agent for Literature Review Writing" and "Dr. Write Academic Writing Mentor" – have been designed and applied to support literature review structuring, research question formulation, paper logic optimization, and international journal writing training, extending support from linguistic accuracy to academic reasoning and research logic.

College Mathematics Series Courses I – – Calculus

Core Fundamental Courses in Mathematics