##Course Description :

The discipline of Industrial engineering utilizing artificial intelligence and machine learning techniques in diverse applications. This course will teach the supervised learning methods, the unsupervised learning methods, as well as the applications of probabilistic graphical learning models. These methods are applicable to quality control, text mining, time series analyses, etc.

(introduction)

Week 1. Motivation and basics

Week 2. Fundamentals of machine learning

Week 3. Naive Bayes Classifier

Week 4. Logistic Regression Classifier

Week 5. Support Vector Machine Classifier

Week 6. Training/Testing and Regularization

Week 7. Bayesian Network

Week 8. K-Means Clustering and Gaussian Mixture Model

Week 9. Hidden Markov Model

Week 10. Sampling Based Inference

 (Advanced)

Week 11. Variational Inference

Week 12. Dirichlet process

Week 13. Gaussian process

Week 14. Neural Network

##Online Lectures Video : You can find Online Lectures on YouTube.

Lecture Video URL : https://www.youtube.com/channel/UC9caTTXVw19PtY07es58NDg