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Please send your suggestions regarding lecture topics to Chuck using this Google Docs form. Questions regarding assignments should be entered in Canvas discussions.


Week Topic Material Reading Assignments
Week 1:
Aug 23, 256
Overview of course. Review of neural networks training and use. 01 Introduction to CS545
02 Searching for Good Weights in a Linear Model
JupyterLab Introduction, watch the video then play with jupyter lab.
The Batch from DeepLearning.AI. Yay, Colorado!
Week 2:
Aug 30, Sept 1
Thursday lecture cancelled. Please watch pre-recorded lecture in Echo360. Quiz1 and A1 questions. Regression with neural networks. 03 Fitting Simple Models Using Gradient Descent in the Squared Error Quiz 1 due Wednesday, August 31, 10:00 PM, in Canvas


Week Topic Material Reading Assignments
Week 3:
Sept 6, 8
Introduction to Neural Networks 04 Introduction to Neural Networks
04a Simple Animations
Activation functions in deep learning: A comprehensive survey and benchmark, Neurocomputing, volume 503, 2022, pp. 92-108
Week 4:
Sept 13, 15
Python classes. A2. 04b Introduction to Python Classes Classes Tutorial A1 Three-Layer Neural Network due Monday, Sept 12th, at 10:00 PM
Week 5:
Sept 20, 22
Optimizers. Autoencoders. 05 Optimizers
05a Collecting All Weights into One-Dimensional Vector for Use in Optimizers
06 Autoencoders
06a Visualizing Weights
Pandas Cheat Sheet A2 NeuralNetwork Class due Thursday, Sept 22nd, at 10:00 PM
Week 6:
Sept 27, 29
A3. Classification 07 Introduction to Classification



Week Topic Material Reading Assignments
Week 11:
Nov 1, 3
Reinforcement Learning for control dynamical systems. Transfer learning in Reinforcement Learning. 18 Reinforcement Learning to Control a Marble
19 Reinforcement Learning Modular Framework
20 Reinforcement Learning to Control a Marble Variable Goal
Faster Reinforcement Learning After Pretraining Deep Networks to Predict State Dynamics, Increased Reinforcement Learning Performance through Transfer of Representation Learned by State Prediction Model A5 Convolutional Neural Networks due Friday, November 4th, at 10:00 PM.
Here are examples of good solutions.
Week 12:
Nov 8, 10
Brain-Computer Interfaces. Linear dimensionality reduction. 21 Linear Dimensionality Reduction with PCA
22 Linear Dimensionality Reduction with Sammon Mapping
Week 13:
Nov 15, 17
Recurrent neural networks. 23 Recurrent Neural Networks
24 Recurrent Network Applications
A6 Reinforcement Learning to Control a Robot due Friday, November 18th, at 10:00 PM. Examples of good solutions
Fall Break:
Nov 21-25
Week 14:
Dec 1
K-means clustering. K-nearest-neighbor classification. 25 K-Means Clustering, K-Nearest-Neighbor Classification


Week Topic Material Reading Assignments
Week 15:
Dec 6, 8
GTA Saira Jabeen summarizes her research. Support Vector Machines. Understanding what a neural net has learned using optimal inputs. 26 Support Vector Machines
An Interpretable Model of Climate Change Using Correlative Learning: Slides
An Interpretable Model of Climate Change Using Correlative Learning: Paper
Alopex: A Correlation-Based Learning Algorithm for Feedforward and Recurrent Neural Networks: Paper
Dec 12-16 Final Exam Week No Exams in this course Project Report, due Monday, December 12th, 10:00 PM. Here is a list of project titles and authors.
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