Information Systems Program
INFO 6105: Data Science Engineering Methods and Tools
Lecture - 4 credits
- Introduces the fundamental techniques for machine learning and data science engineering.
- Discusses a variety of machine learning algorithms, along with examples of their implementation, evaluation, and best practices.
- Lays the foundation of how learning models are derived from complex data pipelines, both algorithmically and practically.
- Topics include supervised learning (parametric/nonparametric algorithms, support vector machines, kernels, neural networks, deep learning) and unsupervised learning (clustering, dimensionality reduction, recommender systems).
- Based on numerous real-world case studies.
Introduces the fundamental techniques for machine learning and data science engineering. Show more.