COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONThis course covers popular topics in computer vision, which includes high-level tasks like image classification, object detection, image segmentation, and low-level tasks like image generation, image enhancement, image-to-image translation, etc.

AIAA 5026 - Computer Vision and Its Applications (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6055) We 09:00AM - 11:50AMRm 149, E1CHEN, Yingcong605640 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONThis is a task-oriented yet interaction-based course, which aims to scrutinize the recent trends and challenges in visual intelligence tasks (from the image restoration to 3D vision tasks). This course will follow the way of flipped-classroom manner where the lecturer teaches the basics; meanwhile, the students will also be focused on active discussions, presentations (lecturing), and hands-on research projects under the guidance of the lecturer in the whole semester. Through this course, students will be equipped with the capability to critically challenge the existing methodologies/techniques and hopefully make breakthroughs in some new research directions.

AIAA 5027 - Chasing Trends for Visual Intelligence: From Restoration to 3D Reconstruction (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6056) Mo 09:00AM - 11:50AMRm 150, E1WANG, Lin308220 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONThis course covers how to program with Python and use it to solve practical problems in Artificial Intelligence. Topics include basic Python usage (e.g., syntax, data structure, etc.) and important packages for data analysis and machine learning applications (e.g., NumPy, SciPy, etc.). The students will be guided to practice on simple artificial intelligence tasks.

AIAA 5031 - Introduction to Computing Using Python (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6059) 22-JAN-2024 - 08-APR-2024
Tu 09:00AM - 11:50AM		Rm 148, E1SUN, Ying6046140 
09-APR-2024 - 10-MAY-2024
Tu 09:00AM - 11:50AM		Lecture Hall CSUN, Ying
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONThis course aims to provide students with an overview of Artificial Intelligence (AI) principles and techniques. Key topics include machine learning, search, game theories, Markov decision process, constraint satisfaction problems, Bayesian networks, etc. Through this course, students will learn and practice the foundational principles, techniques and tools to tackle new AI problems.

AIAA 5032 - Foundations of Artificial Intelligence (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6053) Fr 09:00AM - 11:50AMRm 201, E1LIANG, Junwei302280 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONThis course aims to provide students with key principles and algorithms to build modern autonomous AI systems. Key topics include machine perception, planning and decision-making algorithms. Through this course, students will learn and practice the foundational principles, techniques, and tools to build new autonomous AI systems.

AIAA 5036 - Autonomous AI (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6052) Fr 01:30PM - 04:20PMRm 201, E1LIANG, Junwei302460 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONMachine learning is a cornerstone of AI. The course targets beginners who will learn basic and rigorous machine learning methods, including linear regression, logistic regression, decision trees, naïve Bayes, SVM, unsupervised learning, neural networks, graphical models, EM algorithm. The students will be able to enter more advanced machine learning courses after taking this course.

AIAA 5046 - Fundamentals of Machine Learning (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6054) Mo 09:00AM - 11:50AMRm 233, W1XIE, Sihong409310 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONThis course focuses on the Artificial Intelligence (AI) techniques and applications in multimodal tasks, which involve processing, fusing, and generating contents from multiple data modalities, such as images, videos, text etc. The course will cover the challenges, state-of-the-art methods, as well as hands-on experience in implementing and evaluating multi-modal deep learning models.

AIAA 5048 - Multimodal Artificial Intelligence (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6214) Tu 09:00AM - 11:50AMRm 222, W1WANG, Hao302640 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONIn the era of large-scale deep learning models, multimodal learning based on speech, text, and images is gaining increasing prominence. It holds the potential to facilitate cross-domain applications, improve human-computer interaction, and advance innovation in the field of AI. This course will provide an in-depth exploration of applied deep learning techniques, focusing on their applications in speech processing, natural language understanding, and multimodal data analysis. Students will gain practical experience in building deep learning models for various tasks, including speech recognition, language translation, image analysis, and more. The course covers fundamental concepts, algorithms, and tools in the field of deep learning and emphasizes hands-on projects and real-world applications.

AIAA 5049 - Applied Deep Learning: From Speech to Language and Multimodal Processing (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6215) Tu 09:00AM - 11:50AMRm 233, W1LIU, Li4012280 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONThis course provides a comprehensive introduction to the field of quantum computing. Students will explore fundamental concepts such as quantum bits, quantum circuits, and quantum algorithms. Advanced topics including quantum error correction, quantum information processing, and quantum machine learning will also be covered.

AIAA 5072 - Quantum Computing (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6213) Tu 01:30PM - 04:20PMRm 149, E1WANG, Xin308220 
COURSE INFO
VECTOR[1-3 credit(s)]
DESCRIPTIONAn independent research project carried out under the supervision of a faculty member. Graded P or F.

AIAA 6091A - Independent Study (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
R01 (6339) TBATBATBA202180
Instructor Consent Required
 
COURSE INFO
VECTOR[0 credit]
DESCRIPTIONSeries of seminars presenting research problems currently under investigation, presented by faculty, students, and visiting speakers. Students are expected to attend regularly. Graded P or F.

AIAA 6101 - Artificial Intelligence Seminar I (0 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
T01 (6057) Th 11:00AM - 11:50AMRm 101, W1LIU, Li10033670 
COURSE INFO
VECTOR[0-1-0:1]
DESCRIPTIONSeries of seminars presenting research problems currently under investigation, presented by faculty, students, and visiting speakers. Students are expected to attend regularly. Continuation of AIAA 6101. Graded P or F.

AIAA 6102 - Artificial Intelligence Seminar II (1 unit)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
T01 (6058) Fr 11:00AM - 11:50AMRm 101, W1LIU, Li10032680 
COURSE INFO
DESCRIPTIONMaster's thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned.

AIAA 6990 - MPhil Thesis Research (0 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
R01 (6193) TBATBATBA10036640 
COURSE INFO
DESCRIPTIONOriginal and independent doctoral thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned.

AIAA 7990 - Doctoral Thesis Research (0 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
R01 (6200) TBATBATBA1004960