Course Description
Machine Learning (ML) is the branch of Artificial Intelligence dedicated to teaching computers how to solve tasks by learning from data. This class introduces basic concepts of machine learning through real-world ECE applications. It will cover various learning paradigms such as supervised learning, semi-supervised learning, unsupervised learning, and reinforcement learning. It will also cover classical and state-of-the-art techniques such as linear models, support vector machines, Gaussian mixture models, hidden Markov models, ensemble learning, principal component analysis, and various kinds of deep neural networks. Students will learn the pros and cons of different methods and their suited application scenarios. This course is hands-on with multiple programming assignments and a final project to solve real-world problems.
Course Information
Credits: 4
Lectures: 10:25-11:40 AM on Wednesdays and Fridays
Classroom: CSB 601
Prerequisites: General programming such as ECE-114; MATH 165 linear algebra. Probability and statistics such as ECE 270 is recommended.
Reference Books: The following texts are for references. Some excerpts of them will be required reading materials.
- LWLS - Andreas Lindholm, Niklas Wahlström, Fredrik Lindsten, Thomas B. Schön, Machine Learning: A First Course for Engineers and Scientists, Cambridge University Press, 2022. <PDF>
- WBK - Jeremy Watt, Reza Borhani, Aggelos K. Katsaggelos, Machine Learning Refined: Foundations, Algorithms, and Applications (2nd Edition), Cambridge University Press, 2020. <Companion Website>
- Mitchell - Tom M. Mitchell, Machine Learning, McGraw-Hill Education, 1997. <PDF>
- GBC - Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep Learning, MIT Press. <PDF>
- Géron - Aurélien Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition. <Online Book> accessible with UR email.
Instructor: Zhiyao Duan
Office: CSB 720
Email: zhiyao.duan (at) rochester.edu
Office hour: Wednesdays 3:30-4:30 PM in CSB 720. Additional office hours by appointment.
TAs and Office Hours:
Yujia Yan <yyan22 (at) ur.rochester.edu>, Mondays 3:30-5:30 PM in CSB 504
Huiran Yu <hyu56 (at) UR.Rochester.edu>, Wednesdays 3:30-4:30 PM and Fridays 2-3 PM in CSB 504
Hamed Ajorlou <hajorlou@UR.Rochester.edu>, Tuesdays 1:30-3:30 PM in CSB 701