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\def\classTime{{"MW from 03:00 pm to 04:15 pm",""}}
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\def\classInstructor{{"Xun Jiao",""}}
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\newcommand\semester{Spring 2022}
\newcommand\rsemester{202230}
\newcommand\courseNum{ECE 5400}
\newcommand\courseName{Applied Machine Learning}
\newcommand\courseCoordinator{Xun Jiao}
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\newcommand\meetingDesc{Two 75-minute lectures}
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\newcommand\catalogDesc{In this course, we will study how to develop and apply machine learning methods to different application domains as well as efficient processing of machine learning methods. We will study state-of-the-art machine learning frameworks such as Facebook’s PyTorch or Google’s TensorFlow. We will study the basic knowledge of various machine learning models, such as logistic regression, support vector machine, and neural networks. We will also study optimization techniques such as compression and pruning to enable efficient processing of neural networks. In this course, we will thoroughly examine the emerging trends in industry to understand the underlying research challenges and opportunities. We will implement the machine learning methods and apply on real-world datasets. We will optimize the existing neural networks model using covered techniques and evaluate its effectiveness.}
\newcommand\preReqs{Python, Linear Algebra}
\newcommand\coReqs{None}
\newcommand\coreRequirement{Technical Elective}
\newcommand\courseExpectation{Learn how to develop machine learning models; learn the theoretical aspects of various machine learning algorithms.}
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\newcommand\covTopics{\item Logistic Regression
\item Support Vector Machine
\item Decision Tree
\item Applications on real-world datasets
\item Neural Networks}
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1 & Introduction to ML\\
2 & ML Tasks\\
3 & ML Evaluation\\
4 & Linear Regression\\
5 & Logistic Regression\\
6 & Decision Tree\\
7 & Random Forest\\
8 & Spring Break\\
9 & Multi-Layer Perceptron\\
10 & Convolutional Neural Networks\\
11 & Convolutional Neural Networks\\
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13 & Reinforcement Learning\\
14 & Easter Break\\
15 & Deep Learning\\
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\newcommand\gradingPolicy{Assignments - 40\% \\ 
Midterm - 30\% \\
Final - 30\% \\
\\
Letter grade scale: A(94–100), A–(90–93), B+(87–89), B(83–86), B–(80–82), C+(77–79), C(73–76), C–(70–72),
D+(67–69), D(63–66), D–(60–62), F(<60) \\ 
Late submissions will be assessed a 10% penalty per day.}
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