Machine Learning in MATLAB
The Following algorithms are tentatively planned to be discussed and detailed tutorials/examples will be worked out in the class.
- General Introduction:
- Parametric and Non-parametric Machine LEarning Algorithms
- The Supervised, Unsupervised and semi-supervised Learning
- The Bias-Variance Trade-off
- Overfitting and Underfitting
- Linear Algorithms:
- Gradient Descent.
- Linear Regression.
- Logistic Regression.
- Linear Discriminant Analysis.
- Non-Linear Algorithms:
- Classification and Regression Trees.
- Naive Bayes.
- K-Nearest Neighbors.
- Learning Vector Quantization.
- Support Vector Machines.
- Ensemble Methods:
- Bagged Decision Trees and Random Forest.
- Boosting and AdaBoost.
Saturday: 1st, 8th July; Time: 6:00 – 8:30 PM
Sunday: 2nd , 9th July; Time: 9:00 AM – 5:00 PM (with a lunch-break)