人工智能（英文授课本科）人工智能（英文授课本科）

Bachelor Program in Artificial Intelligence for International Students 

Discipline: Engineering       Major Code: 080901

1. Introduction

The major of Artificial Intelligence is a newly formulated undergraduate major of the Ministry of Education in 2018. Nanjing University of Information Science and Technology (NUIST) is one of the first batch of 35 universities approved to enroll students with this major. NUIST has been recruiting students since September 2019.

Artificial Intelligence is a wide-range major with the background of computer science and information theory. There has gradually been formed a high-level teaching staff of this major. Focusing on the goal of research-led college, this major is oriented to the development of artificial intelligence industry, one of the four emerging strategic industries in China. It is closely related to the frontier scientific problems of cognition and computing, solves major frontier scientific problems, breaks through common key technical bottlenecks. The development of this major is supported by Jiangsu University Sensor Network and Meteorological Equipment and Jiangsu University Information and Communication Engineering.

The major of Artificial Intelligence is closely related with the University's information and communication engineering, computer science and technology, control science and technology, mathematics, atmospheric science, marine science and other advantageous disciplines, forming a new training mode of artificial intelligence + X with the characteristics of our university.

2. Objectives

Facing the needs of new engineering industry and discipline development, this major strengthens the research-led education of artificial intelligence. The training program and curriculum design highlight the characteristics of cultivating innovative research-oriented talents with solid foundation, broad knowledge and excellent ability. This major focuses on cultivating qualified personal quality and good scientific literacy, to ensure the students to master the basic theoretical knowledge of artificial intelligence, be familiar with the basic methods and main tools of artificial intelligence, and can engage in the engineering application or scientific research. The specific objectives are as follows:

Training Objective 1: the ability to apply the basic knowledge of mathematics and artificial intelligence, conceive and design the project products, processes and systems.

Training objective 2: the ability to undertake the design of mathematical model in the fields of artificial intelligence such as natural language processing, computer vision, intelligent meteorology.

Training objective 3: the ability to embody innovative thinking and competitiveness in the practice of artificial intelligence, and consider public safety and health, environmental and social sustainable development.

Training objective 4: have certain organizational management ability and team cooperation and communication quality, can play an effective role in the team.

Training objective 5: have the ability of lifelong learning.

3. Study Duration & Degree

Study duration：4 years, 6 years maximum

Degree: Bachelor of Engineering

4. Introduction to Core Courses

Advanced Maths: Learning basic mathematics theory related to calculus. Have the ability to solve problems with the Calculus methods in various domain. Lay a good foundation for future studies.

Probability and Statistics: This course provides an elementary introduction to probability and statistics with applications. Topics include: basic combinatorics, random variables, probability distributions, Bayesian inference, hypothesis testing, confidence intervals, and linear regression.

Python Programming: This course introduces Python programming language and its applications in deep learning, machine learning. Python is the most popular and widely-used programming language for machine learning. Learning this course will provide a tool for future studies.

Data Structure and Algorithms: Data Structure is a way of collecting and organizing data in such a way that we can perform operations on these data in an effective way. Data Structures is about rendering data elements in terms of some relationship, for better organization and storage. We will also learn basic algorithms and the concepts such as complexity which is vital in future machine learning algorithm analysis.

Machine Learning: Machine Learning is a first-class ticket to the most exciting careers in data analysis today. As data sources proliferate along with the computing power to process them, going straight to the data is one of the most straightforward ways to quickly gain insights and make predictions. Machine learning brings together computer science and statistics to harness that predictive power. It’s a must-have skill for all aspiring data analysts and data scientists, or anyone else who wants to wrestle all that raw data into refined trends and predictions. This is a course that will teach you how to extract and identify useful features that best represent your data, a few of the most important machine learning algorithms, and how to evaluate the performance of your machine learning algorithms.

Optimization: For almost all the human activities there is a desire to deliver the most with the least. For example, in the business point of view maximum profit is desired from least investment; maximum number of crop yield is desired with minimum investment on fertilizers; maximizing the strength, longevity, efficiency, utilization with minimum initial investment and operational cost of various household as well as industrial equipment and machineries. To set a record in a race, for example, the aim is to do the fastest (shortest time). This is what will be studied in the optimization course.

Computer Vision and Pattern Recognition: Computer Vision, often abbreviated as CV, is defined as a field of study that seeks to develop techniques to help computers “see” and understand the content of digital images such as photographs and videos. The problem of computer vision appears simple because it is trivially solved by people, even very young children. Nevertheless, it largely remains an unsolved problem based both on the limited understanding of biological vision and because of the complexity of vision perception in a dynamic and nearly infinitely varying physical world. CV has close relationship with machine learning and pattern recognition. Actually, CV is the fastest developing field in machine learning and deep learning.

Neural Networks and Deep Learning: Deep learning, also known as deep neural networks, is a promising field of machine learning. It targets at feature representation learning, solving a wide range of problems from computer vision to speech recognition. Deep learning often brings state-of-the-art performance in many real-world applications. Its basic theory, its mechanism and many related theoretic can application problems will be investigated in this course.

Introduction to Artificial Intelligence: This course provides a general introduction to Artificial Intelligence, including the history, scope and recent development of the Artificial Intelligence. Not only the introduction of machine learning and deep learning, but also the basic concepts of expert system and knowledge representation will be introduced.

Natural Language Processing: Natural Language Processing, usually shortened as NLP, is a branch of Artificial Intelligence that deals with the interaction between computers and humans using the natural language. The ultimate objective of NLP is to read, decipher, understand, and make sense of the human languages in a manner that is valuable. Most NLP techniques rely on machine learning to derive meaning from human languages. We will introduce basic NLP theories, algorithms and applications. The recent development of NLP and its close relationship with machine learning, deep learning will also be included.

Smart Weather Development Practice: Nanjing University of Information Science and Technology (NUIST) is especially strong at weather science. Hence, we provide an introduction of the combination of Artificial Intelligence of Smart Weather and its applications. The development of weather science is critical in meeting global challenge such global warming. AI technologies will provide an effective and efficient tools and methods for the research of smart weather science.  

Information Retrieval and Data Mining: This course is an emerging interdisciplinary discipline dealing with Information Retrieval and Data Mining techniques. It has undergone rapid development with the advances in mathematics, statistics, information science, and computer science. As the revolutionary development of AI technologies, information retrieval and data mining has also experienced a rapid development. This course will study a wide-range of theoretic and real-world problems providing a general introduction to the recent development of AI technologies application on information retrieval and data mining.

5. Practice

Internship, Graduation Design (Thesis), Training of Applied Artificial Intelligence, Practice of Data Structure and Algorithms, Course Project of Machine Learning.

6. Graduation Requirement

Students shall pass sufficient courses in the program to get no less than 120 credits, pass HSK Level 4 (or above) and pass the Graduation Design (Thesis) defense.

Attachment: Curriculum for International Undergraduate Students in Artificial Intelligence