Maths for ML – 4 Weeks

Course Summary

Course Duration: 4 weeks
Mode: Online / Face-to-Face (Pune)
Instructor Support: Live sessions and Q&A
Timings: 2-hour sessions, thrice a week

This 4-week course focuses on establishing a strong grasp of the mathematical foundations behind ML.
Learners can expect a structured review of key mathematical areas:

Linear Algebra

Probability Basics

Statistics

Calculus (applications in gradient descent)

Optimization Methods

Information Theory



Modules – What Will You Learn?

Module 1: Linear Algebra for ML

Scalars, vectors, matrices, tensors

Matrix operations: addition, multiplication, transpose

Inverse, determinant, rank, trace

Eigenvalues and eigenvectors

Module 2: Probability Basics

Sets, events, sample spaces

Conditional probability, independence

Bayes’ theorem and applications

Random variables, expectation, variance

Common distributions: uniform, binomial, normal, Poisson

Module 3: Statistics for ML

Descriptive statistics: mean, median, mode, variance

Correlation and covariance

Hypothesis testing (null/alternative hypotheses)

p-values and significance

Confidence intervals

Module 4: Calculus for ML

Functions, limits, derivatives

Partial derivatives and gradients

Chain rule and multivariable calculus

Taylor expansion (intuition)

Module 5: Optimization Methods

Convex functions and optimization landscape

Gradient descent and its variants (stochastic, mini-batch)

Convergence and learning rate intuition

Regularization: L1/L2 norms

Module 6: Information Theory & Wrap-Up

Entropy, information gain

Cross-entropy, KL divergence

Relevance in decision trees, clustering, and neural nets

Review and integration of all math topics



Who Should Enroll?

Learners who will benefit the most:

Students bridging the gap between Python basics and applied ML

Undergraduate and postgraduate students aspiring for AI/ML careers

Working professionals from software or IT backgrounds

Non-technical professionals transitioning to AI/ML

Entry-level job seekers specializing in AI/ML