"Machine learning is the field of study that gives computers the ability to learn from experience without being explicitly programmed. This course covers the theory and practical algorithms for machine learning from a variety of perspectives. Topics include decision tree learning, parametric and non-parametric learning, Support Vector Machines, statistical learning methods, unsupervised learning, reinforcement learning and the Bootstrap method. Students will have an opportunity to experiment with machine learning techniques and apply them to solve a selected problem in the context of a term project. The course will also draw from numerous case studies and applications, so that students learn how to apply learning algorithms to build machine intelligence."
Projects:
- Linear Regression: predicting California house values
- Logistic Regressionm Decision Tree: Online Shopping Intension
- ARIMA: Shampoo, AirPassengers
- Classification: Forest Cover Type
- Clustering: Thyroid Disease
- NLP: Text Data
- NN: separate repo