Machine learning is the study that allows computers to adaptively improve their performance with experience accumulated from the data observed. Our two sister courses teach the most fundamental algorithmic, theoretical and practical tools that any user of machine learning needs to know.
Certification :
- Machine learning foundations part I
- Machine learning foundations part II
- Machine learning Techniques
Machine learning is the study that allows computers to adaptively improve their performance with experience accumulated from the data observed. Our two sister courses teach the most fundamental algorithmic, theoretical and practical tools that any user of machine learning needs to know.
The first course of the two would focus more on mathematical tools.
The second course of the two would focus more on algorithmic tools.
- week 1 : The Learning Problem
- week 2 : Learning to Answer Yes/No
- week 3 : Types of Learning
- week 4 : Feasibility of Learning
- Programming assignment-1 : PLA & Pocket Algorithm
- week 5 : Training versus Testing
- week 6 : Theory of Generalization
- week 7 : The VC Dimension
- week 8 : Noise and Error
- Programming assignment-2 : Decision Stump
- week 9 : Linear Regression
- week 10 : Logistic Regression
- week 11 : Linear Models for Classification
- week 12 : Nonlinear Transformation
- Programming assignment-3 : Feature Transform, Linear & Logistic Regression
- week 13 : Hazard of Overfitting
- week 14 : Regularization
- week 15 : Validation
- week 16 : Three Learning Principles
- Programming assignment-4 : Regularization, Ridge Regression & K-fold Validation
The course extends the fundamental tools in "Machine Learning Foundations" to powerful and practical models by three directions, which includes embedding numerous features, combining predictive features, and distilling hidden features.
- week 1 : Linear Support Vector Machine
- week 2 : Dual Support Vector Machine
- week 3 : Kernel Support Vector Machine
- week 4 : Soft-Margin Support Vector Machine
- Programming assignment-5 : Support Vector Machine
- week 5 : Kernel Logistic Regression
- week 6 : Support Vector Regression
- week 7 : Blending and Bagging
- week 8 : Adaptive Boosting
- Programming assignment-6 : AdaBoost Stump & Least Square SVM
- week 9 : Decision Tree
- week 10 : Random Forest
- week 11 : Gradient Boosted Decision Tree
- week 12 : Neural Network
- Programming assignment-7 : Decision Tree & Random Forest
- week 13 : Deep Learning
- week 14 : Radial Basis Function Network
- week 15 : Matrix Factorization
- week 16 : Finale
- Programming assignment-8 : Backpropagation, K-Nearest-Neighbor & K-Means