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19fa-schedule

Schedule

August

Week Topic Material Reading Assignments
Week 1:
August 26 - 30
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 Topic Material Reading Assignments
Week 2:
September 2 - 6
Review of gradients. 02 Searching for Good Weights in a Linear Model
03 Three Gradient Descent Algorithms
Week 3:
September 9 - 13
Class cancelled Thursday, Sept 12th
Implementing neural networks with numpy to predict real-valued variables. Deriving gradients. 04 Scaled Conjugate Gradient
05 Introduction to Gradient Descent for Neural Networks
A1 Gradient Descent due Tuesday, Sept 10th, at 10:00 PM
Week 4:
September 16 - 20
Error gradients for neural networks as matrix equations. 06.1 Gradient Descent for Two-Layer Neural Networks
Hand drawn notes from lecture
Week 5:
September 23 - 27
Introduction to Pytorch and automatic differentation. 07 Automatic Differentation in Pytorch
08 Automatic Differentiation, SGD, and Adam with Pytorch
A2.4 Three Layer Neural Network due Wednesday, Sept 25th, at 10:00 PM

October

Week Topic Material Reading Assignments
Week 6:
September 30 - October 4
Neural Network class. Classification. 09.1 Neural Network Class
10 Classification with Linear Logistic Regression
Week 7:
October 7 - 11
Classification with multiple labels. 11 Classification with Neural Networks Paper on need for causality
Week 8:
October 14 - 18
Convolutional neural networks in numpy, pytorch and tensorflow. 12 Multilabel Classification
13.1 Pytorch nn Module
A3.4 Classification due Wednesday, Oct 16th, at 10:00 PM
Week 9:
October 21 - 25
Convolutional nets.
Reinforcement learning.
14 NeuralNetwork_Convolutional and CIFAR-10
15 Introduction to Reinforcement Learning
Project proposal due at 10 pm Wednesday evening, October 23rd.

November

Week Topic Material Reading Assignments
Week 10:
October 28 - November 1
Reinforcement learning. 16 Reinforcement Learning with Neural Network as Q Function
17 Reinforcement Learning to Control a Marble
Reinforcement Learning: An Introduction, by Richard Sutton and Andrew Barto, 2nd edition A4.4 Convolutional Neural Networks due Wednesday, Oct 30th, at 10:00 PM
Week 11:
November 4 - 8
Transfer learning in Reinforcement Learning
Natural language processing.
Week 12:
November 11 - 15
Natural Language Processing.
Deep learning application development.
18 Embedding With Conv1d.ipynb
19 Embedding Network
20 Transformer Tutorial
How to Code the Transformer in Pytorch by Samuel Lynn-Evans
Week 13:
November 18 - 22
Student presentations.
1. Katherine Haynes: Icing and Low Cloud Detection from the Geostationary Operational Environmental Satellite (GOES-16) Using Neural Networks
2. Hwankook Lee and Erica Shin: Title Unknown
3. Andy Dolan, Tom Cavey, Jason Stock: Augmented Classification Motivated by Neural Network Pitfalls
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7. Zheyi Qin and Zihui Li: Title Unknown
8. Joaquin Cuomo: Video Prediction
9. Vidya Gaddy, Sarah Houlton, Nishant Kashiv, Saurabh Deotale: Playing Atari games using Reinforcement Learning
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A5.1 Control a Marble with Reinforcement Learning due Friday, Nov 22nd at 10:00 PM
Fall Recess:
November 25 - 29

December

Week Topic Material Reading Assignments
Week 14:
December 2 - 6
Student presentations.
1. Sarah Hultin: Making Fake Images with GANs
2. Sam Armstrong, Saloni Choudhary, Brandon Hua: LSTMs on Stock Prices
3. Vihang Narendra Bhosekar, Rakesh Battineedi and Venkata Sai Sudeep Pamulapati: Detection of Higgs Boson
4. Ishani Gowaikar, Lekha Rane and Siddhi Sawant: Title unknown
5. Prerana Ghotge and Soumyadip Roy: GANs and Face Image Augmentation
6. Wei Chen, Zijuan Liu, Ya-Hsin Chen: Title Unknown
7. Eric Wendt, Nicholas Kaufold, Paul Delgado: Embedded Machine Learning
8.
9. Md Al Amin, Long Chen, Nazia Farhat, Upakar Paudel: Hand Written Digit Recognition
10. Jared Crouse, Jarret Flack and Rob Petrovec: Classification of the Million Song Data Set
11. Vishal Anandamani, Brungesh Bangalore Eshwaraiah, Keerthi Dharam: Twitter Sentiment Analysis using Machine Learning Algorithms
12. Rodolfo Amaya: Comparision of ML Techniques on Semi-Conductor Data
Week 15:
December 9 - 13
Student presentations.
1. Hanbai Li, Qingyi Zhao, Marty Wang: Title Unknown
2. Shree Harini Ravichandran and Pavithra Govardhanan: Title Unknown
3. Alperen Tercan, Aniket Tomar, Laksheen Mendis, Sanket Mehrotra: Exploration of Some Reinforcement Learning Ideas.
4. Saptarshi Chatterjee and Sonu Dileep: Facial Authentication using Siamese-like CNN
5.
6. Tim Whitaker: Using GANs to Generate 3D Environments
7. Kevin Bruhwiler, Alexandre Dubois and Jiping Lu: Gravity Wave Localization in Day/Night Band Satellite Imagery
8. Chaitanya Roygaga, Vishal Kuvar and Sandeep Ravipati: A study in Automated Machine Learning: Neural Architecture Search
9. Ujwal Srinivasa: YOLO
10. Fatemeh Hashemi and Pooria Taheri: The Causes of Internal Behavior Shifts in Predictive Coding Networks
11. Dhruv Padalia, Golois Mouelet, Viraj Shastri: Conversational Agent using Seq2Seq network
12.
Finals Week:
December 16 - 20
One more set of Student Presentations. Tuesday 9:00 to noon, Room 452
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Final Project Reports due 10pm Tuesday.
19fa-schedule.txt · Last modified: 2019/12/09 11:44 (external edit)