I am developing a new course Software Engineering for Machine Learning (SER 594) to be offered in Spring 2022.
This course: (a) presents frameworks and tools for developing and incorporating machine-learning components into software systems; and (b) examines the application, adaptation, and extension of software engineering practices to develop and adopt machine-learning-enabled robust, secure, and scalable systems.
Syllabus
Arizona State University.
School of Computing and Augmented Intelligence.
version Spring 2022
Lectures
This course will include 26 lectures:
- Applied machine learning QuickStart
- Data, data processing, data cleaning, and sampling
- Fundamentals on supervised learning
- Fundamentals on unsupervised learning
- Understanding classification,
- Understanding regression
- Understanding clustering
- Neural networks
- Building ML applications
- Software architecture for ML applications
- ML Patterns
- Midterm Review
- Public available Datasets and Repositories
- Machine Learning Libraries
- Comparing Libraries
- Working with Weka,
- Working with DeepLearning4J,
- Working with mallet,
- Working with Encog
- Working with Apache Products
- Assembling applications for:
- Text Mining
- Recommendation Engines
- Pattern (Image) recognition
- Anomaly (outliers) detection
- Final Review