# CS545: Machine Learning

### Sidebar

CS545 - Fall 2021

Instructor
Chuck Anderson

start

This is an old revision of the document!

# Schedule

1. Office Hours with Chuck: Wednesdays, 10:00 - 11:00 AM
2. Office Hours with Dejan: Mondays, 1:00 - 3:00 PM (Starting September 14th)

To use jupyter notebooks on our CS department machines, you must add this line to your .bashrc file:

`export PATH=/usr/local/anaconda/bin:\$PATH`

This is a tentative schedule of CS545 topics for Fall, 2020. This will be updated during the summer and as the fall semester continues.

## August

Week 1:
Aug 24 - Aug 28
Overview of course and the machine learning field. Reminder of how python is used in machine learning. 01 Introduction to CS545
02 Searching for Good Weights in a Linear Model
From Python to Numpy, Chapters 1 - 2
Scipy Lectures, Section 1
Visualization with Matplotlib
Deep Learning, Chapters 1 - 5.1.4

## September

Week 2:
Aug 31 - Sept 4
Help with A1. Review of gradients. Gradient descent with SGD, Adam and SCG, 03 Fitting Simple Models Using Gradient Descent in the Squared Error A1.4 Polynomial Model due Friday, Sept 4th, at 10:00 PM
Week 3:
Sept 7 - Sept 11
Implementing neural networks with numpy to predict real-valued variables. Deriving gradients. 04 Scaled Conjugate Gradient
05 Introduction to Gradient Descent for Neural Networks
Week 4:
Sept 14 - Sept 18
Error gradients for neural networks as matrix equations. Discussion of A2.
Introduction to dashboards with python using streamlit.
06 Introduction to Streamlit streamlit.io
Week 5:
Sept 21 - 25
Use of Optimizers for neural networks. Introduction to Pytorch and automatic differentation. 07 Collect Weights in Vector for Optimizers
A2.2 Multilayer Neural Network due Friday, Sept 25th, at 10:00 PM. Good examples of solutions are available here.

## October

Week 6:
Sept 28 - Oct 2
Neural Network class. 09 Initial Steps towards Defining a NeuralNetwork Class
Week 7:
Oct 5 - Oct 9
Oct 8 Lecture will not meet, but recording will be available.
Help with A3. Dimensionality reduction. 10 Help with A3
11 Low-Dimensional Representations of Data
A3.3 Neural Network Class due Monday, Oct 12, 10:00 PM
Examples of good solutions are available here.
Week 8:
Oct 12 - Oct 16
Brief overview of notes 11.
Introduction to Classification
12 Classification with Neural Networks
Week 9:
Oct 19 - Oct 23
Convolutional neural networks in numpy. 13 NeuralNetwork_Pytorch
14 Introduction to Convolution
Week 10:
Oct 26 - Oct 30
Fully-connected and Convolutional Neural Nets in Pytorch and Tensorflow 15 Convolutional Neural Networks Reinforcement Learning: An Introduction, by Richard Sutton and Andrew Barto, 2nd edition A4.1 Neural Network Classifier due Tuesday Oct 27, at 10:00 PM

## November

Week 11:
Nov 2 - Nov 6
Introduction to Reinforcement Learning. Deep Reinforcement Learning A5
Week 12:
November 11 - 15
Transfer learning in Reinforcement Learning
Week 13:
Nov 16 - Nov 20
Natural Language Processing A6
Nov 23 - Nov 27 Fall Recess!

## December

Week 14:
Nov 30 - Dec 4
Week 15:
Dec 7 - Dec 11
Finals Week:
Dec 14 - Dec 18
A7 due Tuesday, Dec 15th