Machine Learning in MATLAB

Course Contents:

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.
 
Training Schedule:
Saturday-Sunday Weekend Batch (6 days, 22 Hours)
Dates 11th - 12th Aug, 18th- 19th Aug, 25th - 26th Aug 2018
Saturday Time: 2:30 PM - 5:30 PM 
Sunday Time: 11:30 AM - 1:30 PM + TUT: 9:00 - 11:00 AM