Profile

The MSc program in Computer Science and Technology is designed to provide a comprehensive understanding of core disciplines, including Computer Architecture, Software Development, and Application Technology. Through a blend of theoretical learning and practical training, students develop a robust foundation in computer science while exploring cutting-edge advancements in artificial intelligence, data mining, cryptography, and medical imaging.

The program emphasizes fostering innovative thinking, rigorous scientific approaches, and strong engineering skills. Students are prepared to undertake complex research initiatives and lead the implementation of advanced technologies in various fields such as intelligent systems, cloud computing, and visual processing.

With a duration of three years, this program ensures graduates possess a solid academic foundation, outstanding problem-solving abilities, and the adaptability required for leadership roles in both academic and industrial sectors. A minimum proficiency in Chinese (HSK Level 3) is required for graduation, emphasizing the global and culturally enriched learning experience.

Teaching Language:English

Job Prospect

Graduates of the MSc in Computer Science and Technology are equipped for dynamic careers in academia, research, and industry. They can excel as innovators in artificial intelligence, data mining, cloud computing, and cybersecurity. Opportunities include roles in intelligent transportation, medical imaging, robotics, and virtual reality. With the strong theoretical foundation and practical expertise, graduates are also prepared to lead complex engineering projects and drive technological advancements in emerging fields, making the program highly sought-after professionals in the global job market.

Core Courses

Mathematics in Computer

Computer Architecture

Artificial Intelligence and Principles

Machine Learning

Deep Learning

Computer Vision

Introduction to Some Main Courses:

I.  Overview of Mathematics in Computer

This course bridges core mathematical principles with their critical applications in computer science. Students will develop rigorous analytical skills to solve computational problems, design algorithms, and understand the theoretical foundations of modern computing systems. The curriculum emphasizes mathematical tools essential for fields like cryptography, machine learning, and computer graphics.

Key Topics

1. Discrete Mathematics

o Logic, sets, relations, and proofs (induction, contradiction)

o Graph theory (trees, shortest paths, network flows)

o Combinatorics (permutations, probability basics)

2. Linear Algebra & Calculus

o Matrix operations, eigenvalues, and singular value decomposition (SVD)

o Derivatives, integrals, and optimization for machine learning

o Applications in computer graphics (transformations) and data compression

3. Probability & Statistics

o Random variables, distributions, and Bayesian inference

o Hypothesis testing and regression analysis

o Applications in algorithm design (randomized algorithms) and AI

4. Computational Theory

o Finite automata, Turing machines, and computational complexity (P vs. NP)

o Cryptography (modular arithmetic, RSA, elliptic curve cryptography)

o Numerical methods for solving equations and simulations

Learning Outcomes

By the end of this course, students will be able to:

· Formally prove algorithmic correctness and analyze time/space complexity.

· Apply linear algebra to tasks like 3D rendering or dimensionality reduction.

· Model uncertainty using probability for AI/ML systems or network protocols.

· Understand mathematical constraints in cryptography and optimization.

Course Features

· Coding Integration: Implement mathematical concepts using Python/Matlab (e.g., graph algorithms, encryption).

· Case Studies: RSA encryption, PageRank algorithm, neural network backpropagation.

· Industry Relevance: Prepares students for roles in AI research, cybersecurity, or quantum computing.

This course is foundational for computer science students, providing the mathematical rigor required to innovate in cutting-edge technologies and tackle complex computational challenges.

II. Overview of Artificial Intelligence and Principles

This course explores the foundational theories, methodologies, and ethical frameworks of artificial intelligence (AI). Students will examine how machines simulate human intelligence, solve complex problems, and interact with the physical world. The curriculum balances technical rigor with philosophical inquiry, addressing both the capabilities and societal implications of AI systems.

Key Topics

1. Foundations of AI

o Defining intelligence: Turing Test vs. embodied cognition

o Search algorithms (BFS, DFS, A*) and optimization (genetic algorithms)

o Knowledge representation (logic, ontologies, semantic networks)

2. Core AI Techniques

o Machine learning integration (supervised, unsupervised, reinforcement learning)

o Natural language processing (syntax, semantics, transformer models)

o Computer vision (object detection, CNNs, vision transformers)

3. Ethics & Societal Impact

o Bias, fairness, and transparency in AI systems

o AI safety: alignment, robustness, and adversarial attacks

o Legal frameworks (GDPR, AI Act) and existential risks

4. Tools & Frameworks

o Python libraries (OpenAI Gym, spaCy, OpenCV)

o Reinforcement learning platforms (Unity ML-Agents, DeepMind Lab)

o Explainable AI (LIME, SHAP) and ethical auditing tools

Learning Outcomes

By the end of this course, students will be able to:

· Implement classic AI algorithms (e.g., minimax, constraint satisfaction).

· Design AI systems that integrate perception, reasoning, and action.

· Critically evaluate the ethical trade-offs of AI deployment.

· Understand the limits of current AI (e.g., commonsense reasoning, causality).

Course Features

· Hands-on Projects: Build chatbots, game-playing agents, or ethical risk assessment frameworks.

· Debates & Case Studies: Analyze controversies like autonomous weapons, deepfakes, and job displacement.

· Emerging Trends: Explore generative AI (LLMs, diffusion models) and neurosymbolic systems.

This course prepares students for roles in AI engineering, policy-making, or research, equipping them to shape responsible AI innovations in alignment with global societal needs.

III．Overview of Deep Learning

This course dives into the theory, design, and applications of deep neural networks, the driving force behind modern AI breakthroughs. Students will learn to build, train, and deploy state-of-the-art models for tasks like image recognition, natural language processing, and autonomous decision-making. The curriculum emphasizes both foundational mathematics and hands-on implementation, bridging cutting-edge research with real-world problem-solving.

Key Topics

1. Neural Network Foundations

o Perceptrons, activation functions (ReLU, sigmoid), and backpropagation

o Optimization techniques (SGD, Adam, learning rate schedules)

o Regularization (dropout, batch normalization, weight decay)

2. Advanced Architectures

o Convolutional Neural Networks (CNNs) for vision tasks

o Recurrent Neural Networks (RNNs), LSTMs, and Transformers for sequence modeling

o Generative Models (VAEs, GANs, diffusion models) for creative AI

3. Tools & Frameworks

o TensorFlow/Keras and PyTorch for model development

o GPU/TPU acceleration and distributed training strategies

o Deployment tools (ONNX, TensorFlow Lite, Hugging Face)

4. Applications & Ethics

o Computer vision (object detection, segmentation)

o Natural language processing (BERT, GPT, prompt engineering)

o Bias mitigation, model interpretability (Grad-CAM, attention maps)

o Energy efficiency and societal impact of large-scale models

Learning Outcomes

By the end of this course, students will be able to:

· Architect neural networks for supervised, unsupervised, and reinforcement learning tasks.

· Fine-tune pre-trained models (e.g., ResNet, GPT) for domain-specific applications.

· Diagnose and resolve challenges like vanishing gradients or overfitting.

· Critically assess the environmental and ethical implications of deep learning systems.

Course Features

· Hands-on Labs: Train models on datasets like MNIST, CIFAR-10, or COCO.

· Capstone Projects: Build applications such as real-time speech translators, AI art generators, or self-driving car perception systems.

· Industry Insights: Guest lectures on trends like multimodal AI, neuromorphic computing, and LLM alignment.

This course equips students for roles in AI engineering, research, or MLOps, empowering them to harness deep learning’s transformative potential while navigating its technical and societal complexities.