## 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:
•   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.