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Starts 6 June 2025 01:39
Ends 6 June 2025
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Overview
Explore machine learning applications in software development, focusing on data-driven approaches to classify developers and predict project outcomes.
Syllabus
- Introduction to Machine Learning
- Data-Driven Software Development
- Classifying Developers
- Predicting Project Outcomes
- Machine Learning Algorithms
- Implementing Machine Learning Models
- Tools and Platforms
- Ethical Considerations and Best Practices
- Future Trends and Applications
- Capstone Project
Overview of machine learning concepts
Types of machine learning: supervised, unsupervised, and reinforcement learning
The importance of data in machine learning
Data collection and preprocessing methods
Understanding datasets related to software development
Feature selection and engineering
Techniques for developer classification
Using demographic and behavioral data
Case studies on developer classification
Identifying key metrics for project success
Building predictive models for project outcomes
Applications of predictive analytics in project management
Overview of classification algorithms: decision trees, random forests, support vector machines
Regression models for prediction
Clustering techniques and their application
Model training and evaluation
Cross-validation and hyperparameter tuning
Deployment of machine learning models in software environments
Introduction to popular machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch)
Using cloud-based platforms for machine learning (e.g., AWS, Google Cloud AI)
Tool integration in software development pipelines
Understanding biases in algorithms
Ensuring fairness and transparency in model predictions
Responsible use of machine learning in development contexts
Emerging technologies in machine learning for software development
The role of AI in collaborative development environments
Future challenges and opportunities
Define a project scope involving real-world data
Develop a classification or prediction model
Present findings and reflect on the project outcomes
Subjects
Conference Talks