An Introduction to Machine Learning
Undergraduate course, MIT, Department, 2022
This is the class notes of MITx 6.86x Machine Learning with Python-From Linear Models to Deep Learning
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.
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.
Perceptron
Perceptron is the fundamental module of machine learning.