Introduction to Machine Learning
by Leslie Kaelbling & Tomás Lozano-Pérez · MIT OpenCourseWare
Our Verdict
Worth it — with caveatsMIT 6.036 Introduction to Machine Learning is the actual on-campus undergraduate ML course (taught by Leslie Kaelbling and Tomas Lozano-Perez, with Fall 2020 video lectures by Tamara Broderick) released free through MIT OpenCourseWare and the MIT Open Learning Library. It is a rigorous, math-and-algorithms-first survey that builds from perceptrons and margin maximization through linear/logistic regression, neural networks and backpropagation, CNNs/RNNs, clustering, and reinforcement learning, with lecture notes, exercises, weekly labs, and homework. This is genuinely intermediate-level material: the official syllabus expects roughly 12 hours per week over 13 weeks, and you need Python plus comfort with calculus and linear algebra to keep up. It rewards learners who want to understand the mathematics behind the methods rather than just call library functions, but the OCW package is less polished than a paid product and has no certificate or graded feedback. For learners who want MIT-grade depth at zero cost and are willing to self-direct, it is an excellent free option.
Outstanding free, MIT-rigor curriculum that is worth taking IF you already have Python, calculus, and linear algebra and want the math/algorithms behind ML. It is the wrong starting point for absolute beginners or people who only want applied, project-first ML, and the OCW version gives you no certificate, no autograder, and no instructor support.
Best for: Self-directed learners and CS/engineering students who want undergraduate MIT-level rigor and prefer working through derivations, proofs, and problem sets; people with a programming and math background who want to understand WHY ML algorithms work (perceptron, SVM margins, backprop, EM, MDPs) rather than just use a library; anyone wanting a free, high-quality theoretical foundation before deep learning courses.
Skip if: Complete beginners with no calculus or linear algebra, learners who want a gentle, hand-held or purely applied/project-based path, people who need a shareable certificate or graded feedback, and those looking for a quick survey. The OCW/Open Learning Library version has no certificate, no monitored discussion forum, and the lecture quality is reported as uneven, so it is hard going without self-discipline and prior math.
About This Course
MIT course covering perceptrons, feature representation, margin maximization, regression, and neural networks foundations.
What You'll Learn
Curriculum
Introduction to ML; linear classifiers, separability, and the perceptron algorithm.
Maximum-margin hyperplanes, loss functions, regularization, stochastic gradient descent, over-fitting, and generalization.
Linear regression and (in later versions) logistic/linear logistic classification.
Non-linear classification via kernels; recommender problems and collaborative filtering.
Learning features; feed-forward neural networks; deep learning and backpropagation; convolutional neural networks.
Recurrent neural networks; state machines and Markov decision processes.
Generalization/VC-dimension; clustering; generative and mixture models; the EM algorithm; decision trees and random forests.
Hidden Markov Models, Bayesian networks, and probabilistic inference.
Learning to control: reinforcement learning and sequential decision-making.
Prerequisites
- Python programming (e.g., MIT 6.0001/6.00.1x level)
- Single- and multivariable calculus
- Linear algebra (vectors and matrices)
- Basic probability is strongly recommended (helps significantly with the later probabilistic-modeling and EM units)
Instructor
Leslie Kaelbling & Tomás Lozano-Pérez
Instructor · MIT OpenCourseWare
Pros & Cons
Pros
- Genuine MIT undergraduate course materials (lecture notes, slides, full Fall 2020 video lectures, exercises, labs, and homework) released completely free with no paywall
- Strong mathematical and algorithmic depth taught from first principles, frequently praised for breadth across the ML landscape in a single course
- Detailed, well-regarded lecture notes and TA/lab materials that many self-learners cite as the most valuable part
- Builds durable foundations (perceptron, SVM margins, backprop, EM, MDPs) that transfer directly to more advanced deep-learning and research courses
Cons
- No certificate, no autograder, and no monitored discussion forum in the free OCW/Open Learning Library version; you self-assess and self-support
- Steep prerequisites: without solid calculus and linear algebra the material is reported as significantly harder to follow
- Lecture quality is described by learners as uneven, with some lectures lacking critical detail, leaving occasional gaps you must fill yourself
- Theory- and proof-heavy with relatively little hand-holding or applied, portfolio-style project guidance compared with commercial courses
Alternatives To Consider
Frequently Asked Questions
Is Introduction to Machine Learning free?
Yes — Introduction to Machine Learning is free to access. Free with no certificate via MIT OpenCourseWare and the MIT Open Learning Library (optional free enrollment lets you track progress). The closely related credentialed path is the MITx edX course 6.86x 'Machine Learning with Python: from Linear Models to Deep Learning' (part of the Statistics and Data Science MicroMasters), which can typically be audited free but charges a fee for the verified certificate.
Who is Introduction to Machine Learning for?
Self-directed learners and CS/engineering students who want undergraduate MIT-level rigor and prefer working through derivations, proofs, and problem sets; people with a programming and math background who want to understand WHY ML algorithms work (perceptron, SVM margins, backprop, EM, MDPs) rather than just use a library; anyone wanting a free, high-quality theoretical foundation before deep learning courses.
What will you learn in Introduction to Machine Learning?
Formulate supervised learning problems and reason about representation, over-fitting, regularization, and generalization; Linear classifiers from first principles: the perceptron algorithm, maximum-margin hyperplanes, hinge loss, and stochastic gradient descent; Linear and logistic regression, plus non-linear classification with feature transformations and kernels; Neural networks and deep learning, including backpropagation, convolutional networks (CNNs), and recurrent networks (RNNs).
What are the prerequisites for Introduction to Machine Learning?
Python programming (e.g., MIT 6.0001/6.00.1x level); Single- and multivariable calculus; Linear algebra (vectors and matrices); Basic probability is strongly recommended (helps significantly with the later probabilistic-modeling and EM units).
Is Introduction to Machine Learning worth it?
Outstanding free, MIT-rigor curriculum that is worth taking IF you already have Python, calculus, and linear algebra and want the math/algorithms behind ML. It is the wrong starting point for absolute beginners or people who only want applied, project-first ML, and the OCW version gives you no certificate, no autograder, and no instructor support.
How we reviewed this course
This is an independent editorial assessment by Cursarium, based on MIT OpenCourseWare's published course materials and aggregated public learner feedback (last reviewed 2026-06). We have not independently completed the course. Links to providers are standard references, not paid placements.
Sources
- MIT OpenCourseWare 6.036 (Fall 2020) official course page
- MIT Open Learning Library 6.036 (about: prerequisites, 12 hrs/week, 13 weeks, no certificate)
- MIT CSAIL 6.036 syllabus & lecture schedule (week-by-week topics, grading)
- Tamara Broderick - 6.036 Fall 2020 lecture videos & slides (confirms materials and lecture topics)
- Class Central listing for the MITx edX twin 6.86x (4.1/5, ~230 ratings)
- Firsthand learner review of the MITx ML course (difficulty, ~15 hrs/week, uneven lectures)
- DataCamp editorial assessment of MIT 6.036 (best for, strengths, weaknesses, level)