COURSE OVERVIEW
Getting to know the Mathematics and Statistics behind the ML algorithms. Appreciation of these concepts helps in 1. Understanding how the algorithms learn from data 2. Explaining how the results have been obtained 3. Selecting appropriate evaluation parameters Discussion of these topics are done in the context of solving business problems with Machine Learning algorithms and theoretical depth is determined by these considerations. While the Foundation module is adequate for practitioners, researchers in Machine Learning need to take higher level courses as well.
Linear Algebra 
How AIML problems are formulated using the concepts of Scalar, Vector, Matrices and Tensors 
Understanding how Matrix multiplication, identity, inverse and other related concepts such as norm, span, dependence and determinant accelerate AIML algorithms 

Probability 
Probabilistic nature of machine learning  understanding how theory of probability forms the basis of predictive analysis 
Applying probability distributions, marginal and conditional probability, Bayes' rule to solving simple problems and how it is scaled up to machine learning algorithms, Hypothesis testing 

Calculus and Optimisation 
Finding minima and maxima with calculus  how simple principles of calculus when combined with vast computing power can provide solution to complex problems 
Stochastic Gradient Descent Optimisation, Constrained Optimisation and Linear Least Square 

Statistics for Machine Learning 
Measure of Central tendency  Mean, Median, Mode 
Dispersion Measures, Range, Variance, Covariance, Standard Deviation, Zscore, Kurtosis, Skewness 

Correlation and covariance, VIF 

Normal distribution and other types of distribution, z score and cumulative distribution function 

RMSE, MAE, Rsquared 