Machine Learning builds computational systems that learn from and adapt to the data presented to them. It has become one of the essential pillars in information technology today and provides a basis for several applications we use daily in diverse domains such as engineering, medicine, finance, and commerce.
This course covers widely used supervised and unsupervised machine learning algorithms used in industry in technical depth, discussing both the theoretical underpinnings of machine learning techniques and providing hands-on experience in implementing them. Additionally, students will also learn to evaluate effectiveness and avoid common pitfalls in applying machine learning to a given problem.
- Learning to apply course material (to improve thinking, problem solving, and decisions)
- Developing specific skills, competencies, and points of viewneeded by professionals in the field most closely related to thiscourse.
Texts: There is no required text. However, the following books can serve as optional references:
- Python Machine Learning, ISBN: 978-1-78712-593-3•Machine Learning in Action, ISBN: 9781617290183
- Machine Learning with Tensor Flow, ISBN: 9781617293870
- Introduction to Machine Learning with Python, ISBN: 978-1-449-36941-5
- Hands-On Machine Learning with Scikit-Learn and TensorFlow, ISBN: 978-1-491-96229-9
Software:
- Python: We will be using Python libraries available through the scikit learn machine learningpackage.
- SAS Viya: We will also learn to build machine learning models using the SAS Viyaplatform.
- Overview of Machine Learning; Linear Algebra Basics, Intro to Anaconda, Jupyter, Python; Data Cleaning and Preparation
- Supervised Learning: Regression Algorithms, Classification Algorithms
- Dimensionality Reduction Techniques
- Unsupervised Learning: Partitional and Hierarchical Clustering
- Association Rule Learning and Recommender Systems