Course outline
See this page for help on Anaconda, Jupyter, and Python.
Part One
Linear Regression (1/18/21-1/29/21)
Goals
References
Gradient Descent (2/2/21 - 2/12/21)
Goals:
- Learn the basic theory of gradient descent and how it is applied to find maxima and minima of functions
- Apply Gradient Descent to some specific examples.
References
- Gradient Descent html pdf
- Gradient Descent lab - includes datafiles and ipynb file. zip tgz
Probability
Goals:
- Get an introduction to the key ideas from probability that play a role in machine learning.
- Learn about mean, variance, independence, conditional probability, and Bayes theorem.
- Introduce the idea of maximum likelihood.
Logistic Regression
Goals:
- Understand the statistical model underlying logistic regression
- See how the ideas of likelihood and gradient descent combine to solve the logistic regression problem
- Do some sample computations to see Logistic Regression in action.