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Predictive Analytics: Applications of Machine Learning | DBDA.X414
Over the past decade, machine learning has emerged as a critical research area with wide-ranging practical applications in engineering and commerce. Industries as diverse as retail, robotics, manufacturing, and social networking continually provide new examples. How do machine learning engineers know which techniques to implement in this dynamic and evolving field? In this fundamentals course, we prepare you to answer that question.
Through hands-on activities you will begin to understand, build, and test machine learning techniques. You will receive a broad introduction to the key learning methods, including regression, classification, clustering, and recommender systems. The focus is not on individual algorithms but rather the ideas that make them work.
In addition to reviewing the steps involved in building predictive models, including data collection, feature selection, algorithms and evaluation, you will learn from case studies to fine tune the performance of these models and plan for practical implementation issues.
Using Python/R you will work with machine learning concepts, terms, and methodology gaining an intuitive understanding of the mathematics underlying it by building actual applications. The techniques you’ll learn are the foundation for real-world applications such as classification, regression, image analysis, and bioinformatics. Pseudocode will be provided for most of the algorithms. Homework assignments are designed for in-depth practice.
By the end of the course, you will have a basic understanding of machine learning techniques and know how to apply basic machine learning tools in practical situations. You will have experience with methods to formulate and solve machine learning problems in Python/R and will have completed several assignments and a project using supervised or unsupervised learning algorithms.Topics include:
- Review of R/Python (NumPy, SciPy, pandas, Scikit-Learn, Matplotlib)
- Fundamentals of machine learning
- Linear regression and logistic regression
- K-nearest neighbors (k-NN) and support vector machine
- Bayesian classifiers: naïve Bayes
- Decision tree and random forests
- Unsupervised learning
- Dimensional reduction: PCA, ridge regression and lasso methods
- Performance evaluation
- Brief introduction to deep learning
Skills Needed: Skills Needed: Basic programming experience is recommended. Python/R experience can be helpful. Basic knowledge of probability and statistics is required.
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