6.036 Introduction to Machine Learning is a foundational course that teaches students the essential principles of machine learning. This class helps students gain an understanding of the concepts, algorithms and techniques used in machine learning systems. The topics covered include supervised and unsupervised learning, reinforcement learning, Bayesian networks, graphical models, deep learning, optimization and simulation. By gaining a strong understanding of these concepts, students will be able to use Machine Learning techniques to create powerful computer programs that can make decisions based on data or take new forms of input.

##### Table Of Content:

- Introduction to Machine Learning | Electrical Engineering and ...
- 6.036 Introduction to Machine Learning: 6.036 Introduction to ...
- Course | 6.036 | MIT Open Learning Library
- 6.036 Introduction to Machine Learning, Fall 2020 - Tamara Broderick
- Introduction to Machine Learning | MIT Open Learning Library
- 6.036 Spring 2022
- 6.036 Introduction to Machine Learning
- What is it like to take 6.036 (Introduction to Machine Learning) at MIT ...
- 6.036 Lecture Notes
- 6.036: Intro to Machine Learning Introduction 1 September 10, 2019

### 1. Introduction to Machine Learning | Electrical Engineering and ...

https://ocw.mit.edu/courses/6-036-introduction-to-machine-learning-fall-2020/

This course introduces principles, algorithms, and applications of machine learning from the point of view of modeling and prediction.

### 2. 6.036 Introduction to Machine Learning: 6.036 Introduction to ...

https://canvas.mit.edu/courses/7509/pages/6-dot-036-introduction-to-machine-learning

Welcome to 6.036 Introduction to Machine Learning! ... You cannot understand machine learning without understanding vectors, dot products, matrices and partial ...

### 3. Course | 6.036 | MIT Open Learning Library

https://openlearninglibrary.mit.edu/courses/course-v1:MITx+6.036+1T2019/course/

Introduction to Machine Learning · Welcome to 6.036 · Week 1: Basics · Week 2: Perceptrons · Week 3: Features · Week 4: Margin Maximization · Week 5: Regression · Week ...

### 4. 6.036 Introduction to Machine Learning, Fall 2020 - Tamara Broderick

https://tamarabroderick.com/ml.html

In Fall 2020 I gave the lectures for MIT's 6.036 course: Introduction to Machine Learning. Below is a set of links to those lectures.

### 5. Introduction to Machine Learning | MIT Open Learning Library

https://openlearninglibrary.mit.edu/courses/course-v1:MITx+6.036+1T2019/about

This course introduces principles, algorithms, and applications of machine learning from the point of view of modeling and prediction.

### 6. 6.036 Spring 2022

https://introml.odl.mit.edu/

Jan 29, 2022 ... 6.036: Introduction to Machine Learning Spring 2022. You are not logged in. If you are a current student, please Log In for full access to the ...

### 7. 6.036 Introduction to Machine Learning

https://courses.csail.mit.edu/6.036/

As a discipline, machine learning tries to design and understand computer programs that learn from experience for the purpose of prediction or control. In this ...

### 8. What is it like to take 6.036 (Introduction to Machine Learning) at MIT ...

https://www.quora.com/What-is-it-like-to-take-6-036-Introduction-to-Machine-Learning-at-MIT

I took 6.036 2014 and really enjoyed the class. As Anvisha already said, the lecture notes were very detailed and the lecturers were very engaging.

### 9. 6.036 Lecture Notes

https://phillipi.github.io/6.882/2020/notes/6.036_notes.pdf

Introduction. The main focus of machine learning is making decisions or predictions based on data. There are a number of other fields with significant ...

### 10. 6.036: Intro to Machine Learning Introduction 1 September 10, 2019

http://www.mit.edu/~lindrew/6.036.pdf

6.036: Intro to Machine Learning. Lecturer: Professor Leslie Kaelbling. Notes by: Andrew Lin. Fall 2019. Introduction. Lectures are posted online as well, ...

## What is 6.036 Introduction to Machine Learning?

6.036 Introduction to Machine Learning is a foundational course that teaches students the essential principles of machine learning such as supervised and unsupervised learning, reinforcement learning, Bayesian networks, graphical models, deep learning, optimization and simulation.

## What topics are covered in this course?

This course covers topics such as supervised and unsupervised learning, reinforcement learning, Bayesian networks, graphical models, deep learning optimization and simulation.

## What skills can I gain from taking this course?

By taking this course you will gain an understanding of fundamental concepts such as supervised and unsupervised machine learning algorithms, Bayesian networks for exploring data relationships and making predictions based on them. You will also learn about deep neural networks which can be used for more complex tasks such as image recognition or natural language processing tasks. Furthermore you will gain an understanding of optimization methods used for training various machine-learning algorithms as well as understand the concept of reinforcement learning which allows machines to learn through trial-and-error without explicit instruction from humans.

## How can I apply what I learn in this course?

By mastering the topics discussed in 6.036 Introduction to Machine Learning you will be able to apply them in your own projects or work situation by using machine-learning tools like TensorFlow or Scikit-Learn to create applications that make decisions based on data or take new forms of input.

**Conclusion:**

In conclusion 6.036 Introduction to Machine Learning is an excellent foundational class for those wanting to pursue careers in artificial intelligence or those who simply wish to have a better understanding of how machines learn through data analysis and modelling techniques such as supervised/unsupervised/reinforcement/Bayesian/graphical/deep network models and optimization methods . With this knowledge you can use various open source tools like TensorFlow or Scikit-Learn to develop applications that make decisions informed by data inputs or take new forms of input.