Course Outcomes

• Understand fundamentals of Linear Algebra like matrix operations, solving linear equations, row reduction, LU decomposition, inverse, vector space, subspace, dependence, independence and dimension
• Understand concepts of calculus like limits, continuity, differentiation and integration.
• Understand concepts of probability and stats like distributions, conditional probability, Bayes theoram, Data summarization, correlation and regression analysis.
• Understand concepts of set theory, graphs, propositional logic and counting for Computer science.
• Understand the practical applications of each mathematical topic.

Approach

• Use Jupyter notebooks and Python libraries like Numpy and Scipy to apply concepts of Linear Algebra and Calculus to practical problems.
• For Probability and Statistics, work with real-world datasets and use Python libraries like pandas to calculate various metrics and visualize data.
• For Discrete Mathematics, solve algorithmic problems using the learned concepts through Python after understanding the basics.

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