This course introduces the fundamental theory/background knowledge of prevalent machine learning (ML) and computer vision (CV) algorithms. Relevant applications in the broad domain of the engineering community will be introduced to motivate the students. The first half of the semester will focus on the reasoning of artificial intelligence, several ML algorithms, model evaluation, deep learning (DL) and reinforcement learning. The rest of the semester will have emphasis on the reasoning of image processing, image feature extractions and pairing, as well as image-based sensing. After taking this course, students are expected to be equipped with basic knowledge and implementation skills to develop ML, DL or CV based approaches for applications in engineering.

Upon taking this course, students are anticipated to be well-prepared in the following items: 1. Understand the fundamental principles that support the ML/DL algorithms. 2. Be able to reasoning the performance of ML/DL models. 3. Be able to implement ML/DL algorithms. 4. Understand the fundamental principles that support the CV algorithms. 5. Understand the image representations of the world. 6. Be able to implement CV algorithms.

Prerequisites: Calculus, Computer Programming

4hrs

Several excellent online sources are: 1. A Course in Machine Learning, electronic source available at: http://ciml.info/ 2. Christopher Bishop (2006), Pattern Recognition and Machine Learning, Springer 3. Goodfellow et. al (2016), Deep Learning, MIT Press, electronic source available at: https://www.deeplearningbook.org/