Foundations of Machine Learning Algorithms: Pen-Paper Calculations

"Foundations of Machine Learning (Algorithms): Pen-paper calculations " course is a non-coding course, which is a MUST for ALL persons desirous of learning Machine Learning from mathematical and algorithmic point of view. We will focus more on the theoretical aspects of the algorithms, parameters and hand-calculations will be done on dummy data step-by-step. In some cases, to automate the calclations, we will be using MS Excel.

Course Highlights:
  • Clear algorithm explainations that help you to understand the principles that underlie each technique.
  • The step-by-step algorithm workout on black-board to show you exactly how each model learns.
  •  Real worked examples so that you can see exactly the numbers in and the numbers out, there’s nowhere for the details to hide. 
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
Essential Mathematics for Machine Learning - Part – A: Basic Probability
  •  Basic Definitions
  •  Even & odds of an event
  •  Bayes Theorem & applications
  •  Probability Distribution Functions
Essential Mathematics for Machine Learning - Part – B: Basic Statistics
  •  Mean, Mode, Median
  •  Standard Deviation, Variance
  •  Correlation and Correlation-coefficient
  •  Standard Statistical Distributions
Essential Mathematics for Machine Learning - Part – C: Linear Algebra

       1.      Identity Matrix and Diagonal Matrices

2.      Transpose, Inverse, Trace, Norms and Determinant of Matrices

3.      Symmetric & Orthogonal Matrices

4.      Linear Independence and Rank

5.      Eigenvalues and Eigenvectors of Symmetric Matrices

9.      Matrix Multiplication

10.    Operations and Properties

Essential Mathematics for Machine Learning - Part – D : Matrix Calculus

       1.      Gradients and Hessians of Quadratic and Linear Functions

2.      Least Squares

3.      Gradients of the Determinant

4.      Eigenvalues as Optimization

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 14th - 15th July, 21 - 22 July, 28 - 29th July 2018
Saturday Time :  6:00 PM - 9.00 PM 
Sunday Time :  8:00 AM-11:00 AM + TUT 11:30 AM - 1:30 PM

 

For registrations contact: 8169 543 099 or 9860 246 128.