Courses Taught by Professor Yi Ma

University of Hong Kong

• DATA8014: Principles of Deep Representation Learning, IDS@HKU, Fall 2024.
• Summer Institute: Towards AI by Deep Neural Networks, IDS@HKU, Summer 2024.
• DATA8001: High-Dimensional Data Analysis, IDS@HKU, Fall 2023.
   

University of California, Berkeley (and TBSI)

• EECS16B: Designing Information Devices and Systems II, EECS@UC Berkeley, Fall 2022.
• Summer Course on High-Dimensional Data Analysis, Tsinghua-Berkeley Shenzhen Institute (TBSI), remote, June 2022.
• CS294: Geometry and Learning for 3D Vision (Course Piazza and a tentative Syllabus.pdf), EECS@UC Berkeley, Spring 2022.
• EECS106B/206B: Robotic Manipulation and Interaction, co-teach with Prof. Shankar Sastry, EECS@UC Berkeley, Spring 2022.
• EECS208: Computational Principles for High-Dimensional Data Analysis (Course Website and Course Piazza), EECS@UC Berkeley, Fall 2021.
• EECS 106A: Introduction to Robotics, EECS@UC Berkeley, Fall 2021.
• Summer Course on Geometry and Learning for 3D Vision, Tsinghua-Berkeley Shenzhen Institute (TBSI), July 2021.
• EECS290-005: Integrated Perception, Learning, and Control, co-teach with Prof. Jitendra Malik, Shankar Sastry, and Claire Tomlin, EECS@UC Berkeley, Spring 2021.
• EECS106B/206B: Robotic Manipulation and Interaction, co-teach with Prof. Shankar Sastry, EECS@UC Berkeley, Spring 2021.
• EE290-002: High-Dimensional Data Analysis with Low-Dimensional Models (a tentative syllabus.pdf), EECS@UC Berkeley, Fall 2020.
• Summer Course on High-Dimensional Data Analysis, Tsinghua-Berkeley Shenzhen Institute (TBSI), remote, July 2020.
• CS294: Geometry and Learning for 3D Vision (a tentative syllabus.pdf), EECS@UC Berkeley, Spring 2020.
• EE290-001: High-Dimensional Data Analysis with Low-Dimensional Models (a tentative syllabus.pdf), EECS@UC Berkeley, Fall 2019.
• Summer Course on High-Dimensional Data Analysis, Tsinghua-Berkeley Shenzhen Institute (TBSI), Shenzhen, China, July 4 - 13, 2019.
• EE290T: High-Dimensional Data Analysis with Low-Dimensional Models, EECS@UC Berkeley, Fall 2018.
• EE221: Linear System Theory, EECS@UC Berkeley, Fall 2018.
• Summer Course on High-Dimensional Data Analysis, Tsinghua-Berkeley Shenzhen Institute (TBSI), Shenzhen, China, July 3 - 14, 2018.
   

ShanghaiTech University

• Deep Learning, SIST@ShanghaiTech, Fall 2016.
• Computer Vision: 3D Reconstruction, SIST@ShanghaiTech, Spring 2016.
• Compressive Sensing, SIST@ShanghaiTech, Fall 2015.
• Computer Vision, SIST@ShanghaiTech, Spring 2015.
• Introduction to Information Science and Technology, SIST@ShanghaiTech, Spring 2015.
• Compressive Sensing, SIST@ShanghaiTech, Fall 2014.
• Generalized Principal Component Analysis, SIST@ShanghaiTech, Spring 2014.
   

Microsoft Research Asia

• No teaching duties (from Spring 2009 to Fall 2014)
   

University of Illinois at Urbana-Champaign

• ECE598YM: Sparse Representation and High-Dimensional Geometry, Fall 2008.
• ECE553: Optimum Control Systems, Spring 2008.
• ECE515: Control Systems Theory and Design, Fall 2007.
• On sabbatical leave: Visiting Professor at EECS Department, UC Berkeley, Spring 2007.
• On personal leave: Visiting Researcher at Microsoft Research Asia, Beijing, China, Fall 2006.
• ECE586YM: Estimation and Segmentation of Hybrid Models, Spring 2006.
• ECE515: Control Systems Theory and Design, Fall 2005.
• ECE/GE528: Analysis of Nonlinear Systems, Spring 2005.
• ECE597: Individual Study - Computer Vision, Spring 2005.
• ECE598: An Invitation to 3-D Vision: From Images to Geometric Models, Fall 2004.
• ECE210: Analogue Signal Processing, Spring 2004.
• ECE415: Control Systems Theory and Design, Fall 2003.
• ECE453: Optimum Control Systems, Spring 2003.
• ECE497: 3-D Vision: A Multiple View Approach, Fall 2002.
• ECE210: Analogue Signal Processing, Spring 2002.
• ECE434: Random Processes, Fall 2001
• ECE497: Advanced Geometric Approaches to Computer Vision, Spring 2001.
• ECE415: Control Systems Theory and Design, Fall 2000.