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Math for Machine Learning 2020-04-15T15:56:11+00:00

# Math for Machine Learning

This course is designed to build an intuitive understanding of maths required / involved / related in Data Science and Machine Learning

## Key Features

• Gain experience with handson exercises

• Instructor led training

• Flexibility of learning in class or online

• Learn by doing – Assignments, Tasks

• Curriculum – par with Industry

• Innovative Approach – Discussions, Quiz, webinar

• Solutions for your skill gaps

• Upskilling and Level Setting

• Dedicating mentoring sessions from industry experts

• Career Support

• Industry valued Certificate

# Schedule

BatchModeTimeFee
Weekends/ WeekdaysOnline Training
Instructor Led
FlexibleINR 12000/-
Weekends/ WeekdaysClassroom TrainingFlexibleINR 10000/-

9841557655

# Curriculum

#### Linear Algebra

• Systems of Linear Equations
• Matrices
• Solving Systems of Linear Equations
• Vector Spaces
• Linear Independence
• Basis and Rank
• Linear Mappings
• Affine Spaces

#### Analytic Geometry

• Norms
• Inner Products
• Lengths and Distances
• Angles and Orthogonality
• Orthonormal Basis
• Orthogonal Complement
• Inner Product of Functions
• Orthogonal Projections
• Rotations

#### Matrix Decompositions

• Determinant and Trace
• Eigenvalues and Eigenvectors
• Cholesky Decomposition
• Eigen decomposition and Diagonalization
• Singular Value Decomposition
• Matrix Approximation
• Matrix Phylogeny

#### Vector Calculus

• Differentiation of Univariate Functions
• Useful Identities for Computing Gradients
• Backpropagation and Automatic Differentiation
• Higher-Order Derivatives
• Linearization and Multivariate Taylor Series

#### Descriptive statistics

• Cases, variables and levels of measurement
• Data matrix and frequency table
• Graphs and shapes of distributions
• Measures of central tendency Mode, median and mean
• Variance and standard deviation
• Range, interquartile range and box plot
• Z-scores
• Dispersion

#### Inferential Statistics

• Null hypothesis testing
• P-values
• Confidence intervals and two-sided tests
• Power
• Two independent proportions, means
• Two dependent proportions, means
• Controlling for other variables
• Categorical association and independence.
• The Chi-squared test6m
• Interpreting the Chi-squared test
• Chi-squared as goodness-of-fit
• The Chi-squared test – sidenotes
• Fisher’s exact test

#### Probability and Distributions

• Construction of a Probability Space
• Discrete and Continuous Probabilities
• Sum Rule, Product Rule, and Bayes’ Theorem
• Summary Statistics and Independence
• Gaussian distribution
• Conjugacy and the Exponential Family
• Change of Variables/Inverse Transform

#### Continuous Optimization    