An in-depth introduction to the field of machine learning, from linear models to deep learning and reinforcement learning, through hands-on Python projects. -- Part of the MITx MicroMasters program in Statistics and Data Science.

Syllabus

Lectures :

Introduction

Linear classifiers, separability, perceptron algorithm

Maximum margin hyperplane, loss, regularization

Stochastic gradient descent, over-fitting, generalization

Linear regression

Recommender problems, collaborative filtering

Non-linear classification, kernels

Learning features, Neural networks

Deep learning, back propagation

Recurrent neural networks

Generalization, complexity, VC-dimension

Unsupervised learning: clustering

Generative models, mixtures

Mixtures and the EM algorithm

Learning to control: Reinforcement learning

Reinforcement learning continued

Applications: Natural Language Processing

Projects :

Automatic Review Analyzer

Digit Recognition with Neural Networks

Reinforcement Learning

Institution: MITx

Subject: Computer Science

Level: Advanced

Prerequisites:

6.00.1x or proficiency in Python programming

6.431x or equivalent probability theory course

College-level single and multi-variable calculus

Vectors and matrices

Associated programs:

MicroMasters® Program in Statistics and Data Science

MicroMasters® Program in Statistics and Data Science (General track)

Language: English

Video Transcript: English

Associated skills: Machine Learning, Artificial Neural Networks, Statistics, Data Science, Forecasting, Recommender Systems, Machine Learning Algorithms, Physics, Experimentation, Support Vector Machine, Sales, Python (Programming Language), Consumer Behaviour, Algorithms, Linear Model, Deep Learning, Prediction, Reinforcement Learning

What you\'ll learn

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.