Welcome to 6.86x Machine Learning with Python–From Linear Models to Deep Learning.

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Machine learning methods are commonly used across engineering and sciences, from computer systems to physics. Moreover, commercial sites such as search engines, recommender systems (e.g., Netflix, Amazon), advertisers, and financial institutions employ machine learning algorithms for content recommendation, predicting customer behavior, compliance, or risk.

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 course, you will learn about principles and algorithms for turning training data into effective automated predictions. We will cover:

• Representation, over-fitting, regularization, generalization, VC dimension;

• Clustering, classification, recommender problems, probabilistic modeling, reinforcement learning;

• On-line algorithms, support vector machines, and neural networks/deep learning.

You will be able to:

• Understand principles behind machine learning problems such as classification, regression, clustering, and reinforcement learning

• Implement and analyze models such as linear models, kernel machines, neural networks, and graphical models

• Choose suitable models for different applications

• Implement and organize machine learning projects, from training, validation, parameter tuning, to feature engineering

You will implement and experiment with the algorithms in several Python projects designed for different practical applications.

You will expand your statistical knowledge to not only include a list of methods, but also the mathematical principles that link these methods together, equipping you with the tools you need to develop new ones.

Time Committment :

The corresponding on-campus class is designed so that MIT students with appropriate prerequisites in mathematics spend on average 12 hours per week on lectures, homework, and coding exercises. You should expect a comparable effort, or more if you need to catch up on background material.

In a typical week, there will be roughly 2 hours of lecture clips, but it might take you 3.5 hours when you add the time spent on exercises. Finally, expect about 4-6 hours spent on the homeworks, and 5-14 hours on the biweekly projects.

Grading policy

Your overall score in this class will be a weighted average of your scores for the different components, with the following weights:

• 16% for the lecture exercises (divided equally among the 16 out of 19 lectures)
• 1% for the Homework 0
• 12% for the homeworks (divided equally among 4 (out of 5) homeworks)
• 2% for the Project 0
• 36% for the Projects (divided equally among 4 (out of 5)
• 13% for the Midterm exam (timed)
• 20% for the final exam (timed)

To earn a verified certificate for this course, you will need to obtain an overall score of 60% or more of the maximum possible overall score.