##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