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: 1st, 8th July; Time: 6:00 – 8:30 PM

Sunday: 2nd , 9th July; Time: 9:00 AM – 5:00 PM (with a lunch-break)

All the information regarding Fees, discounts and Registration Form is available here.

Page Last Updated: Monday 12-Jun-2017 14:24:25 IST