This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition.
Topics include:
- Supervised learning (linear regression, logistic regression, parametric/non-parametric algorithms, support vector machines, kernels, neural networks)
- Unsupervised learning (k-means, PCA, clustering, dimensionality reduction, recommender systems, anomaly detection, deep learning)
- Best practices in machine learning (bias/variance theory; evaluation of learning algorithms, learning curves, error analysis, ceiling analysis, innovation process in machine learning and AI)
The course also draws from numerous case studies and applications, allowing me to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.