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Introduction to Machine Learning
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Welcome to 6.036
6.036 Information
Self-Assessment
Week 1: Basics
Introduction to ML
Linear classifiers
Week 1 Exercises
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Homework 1
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Week 2: Perceptrons
The Perceptron
Week 2 Exercises
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Week 2 Lab
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Week 2 Homework
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Week 3: Features
Feature representation
Week 3 Exercises
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Week 3 Lab
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Week 3 Homework
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Week 4: Margin Maximization
Logistic Regression
Gradient Descent
Week 4 Exercises
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Week 4 Lab
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Week 4 Homework
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Week 5: Regression
Regression
Week 5 Exercises
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Week 5 Lab
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Week 5 Homework
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Week 6: Neural Networks I
Neural Networks
Week 6 Exercises
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Week 6 Lab
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Week 6 Homework
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Week 7: Neural Networks II
Making NN's Work
Week 7 Exercises
Week 7 Homework
Week 8: Convolutional Neural Networks
Convolutional Neural Networks
Week 8 Exercises
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Week 8 Lab
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Week 8 Homework
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Week 9: State Machines and Markov Decision Processes
Sequential models
Week 9 Exercises
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Week 9 Lab
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Week 9 Homework
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Week 10: Reinforcement Learning
Reinforcement learning
Week 10 Exercises
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Week 10 Lab
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Week 10 Homework
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Week 11: Recurrent Neural Networks
Recurrent Neural Networks
Week 11 Exercises
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Week 11 Lab
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Week 11 Homework
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Week 12: Recommender Systems
Recommender systems
Week 12 Exercises
Week 12 Lab
Week 12 Homework
Week 13: Decision Trees and Nearest Neighbors
Decision Trees and Nearest Neighbors
Week 13 Exercises
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Week 13 Lab
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