Essential Mathematics for Machine Learning

This a completely non-coding course where you will be taught about the basic mathematics underlying some of the machine learning algorithms. Also, you will yourself workout these algorithms on your copy (using calculator only) on dummy datasets. So you very well know how each algorithm actually works in and out.

Duration: 2.5 - 3.0 hours Black-board Teaching

Part – A: Basic Probability

  • Basic Definitions
  • Even & odds of an event
  • Bayes Theorem & applications
  • Probability Distribution Functions
Part – B: Basic Statistics
  • Mean, Mode, Median
  • Standard Deviation, Variance
  • Correlation and Correlation-coefficient
  • Standard Statistical Distributions

Part – C: Linear Algebra

  1. Matrix Multiplication
  2. Operations and Properties
      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
  3. Matrix Calculus
      1. Gradients and Hessians of Quadratic and Linear Functions
      2. Least Squares
      3. Gradients of the Determinant
      4. Eigenvalues as Optimization

Part – D: Applications to Machine Learning

  • Linear Regression: Least Squares Solution
  • Stochastic Gradient Descent for Linear Regression
  • Logistic Regression
  • Linear Discriminant Analysis
  • Naïve Bayes Classifier

 Instructor/Presenter: Prashant Sahu.

All participants will receive Certificate of Participation.


Registration Details:

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Step - 1: Before you start filling up the registration form, You have to Pay the Registration Fee of Rs. 200/- by any of the below methods (NEFT / PayTM / Credit Card/Debit Card/ NetBanking)
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For any queries Contact:  +91 - 988 171 9501

Page Last Updated: Monday 09-Oct-2017 14:09:37 IST