CS 342 - Neural networks - Fall 2018

meets
TTh 10am-11am UTC 3.124
Fr 10am-11am / 11pm-noon ECJ 1.204

instructor Philipp Krähenbühl ( philkr (at) utexas.edu )
office hours Tue 11am-12pm GDC 4.824
Please only visit during office hours or with appointment (or if I didn’t reply your email after the third try)

TA
Dian Chen ( dchen (at) cs.utexas.edu )
office hours Mon 11am-noon GDC 1.302 Desk 1
Ankur Garg ( ankgarg (at) cs.utexas.edu )
office hours Mon 4pm-5pm GDC 1.302 Desk 1

We will use piazza for questions and canvas homework.

Schedule

class section

Date	Topic	Concepts	Slides	Corresponding book chapters (optional; see below)	Notes and due dates
Aug 30	Introduction	tensors layers	intro	Background: Ch 1-5
Aug 31	Installing pytorch and building the first compute graph		slides tutorial		HW1 out
Sep 4	Simple networks	fully connected ReLU	slides exercise 1 solution
Sep 6	Output transformations and loss functions	sigmoid softmax log likelihood L1/L2 loss	slides	5.9	HW1 due 11:59pm solution
Sep 7	Logistic regression and MLP		slides Tutorial		HW2 out
Sep 11	Back propagation	back prop	slides exercise 2 solution	Ch 6
Sep 13	Stochastic gradient descent	SGD momentum	slides		HW2 due 11:59pm solution
Sep 14	Regression vs classification		slides		HW3 out
Sep 18	Activation functions + Initialization	LeakyReLU PReLU Gaussian Init Xavier Init	slides	Ch 6
Sep 20	Convolutions + Pooling	Conv2D Avg Pooling Max Pooling striding / padding	slides	9.1-9.3	HW3 due 11:59pm solution
Sep 21	ConvNets		slides tutorial		HW4 out
Sep 25	Overfitting	early stopping data augmentation dropout parameter sharing ensembles	slides exercise 3 solution 3	7.4, 7.8, 7.9, 7.11, 7.12
Sep 27	Optimization tips and tricks	weight regularization batch normalization	slides exercise 4 solution 4	7.1-7.3	HW4 due 11:59pm solution
Sep 28	Regularization and normalization		slides tutorial		HW5 out
Oct 2	Popular Architectures I	LeNet AlexNet VGG auxillary losses	slides exercise 5 solution
Oct 4	Popular Architectures II	GoogLeNet ResNet DenseNet residual connections	slides		HW5 due 11:59pm solution
Oct 5	ResNets		slides		HW6 out
Oct 9	Object detection	ROI Pooling R-CNN Fast R-CNN YOLO	slides exercise 6 solution
Oct 11	Segmentation	up-convolution FCN	slides		HW6 due 11:59pm solution
Oct 12	Fully convolutional networks		slides tutorial		HW7 out
Oct 16	Visualization and understanding	gradients and saliency	slides exercise 7 solution 7
Oct 18	Generative models	reparametrization trick adversarial loss	slides	20.10, 20.12, 20.13	HW7 due 11:59pm solution
Oct 19	Upconvolution and deep rendering		slides tutorial		HW8 out
Oct 23	Recurrent Network I	RNN LSTM GRU	slides exercise 8 solution 8	10.1-10.11
Oct 25	Recurrent Network II	Temporal CNNs	slides		HW8 due 11:59pm solution
Oct 26	Future prediction		slides tutorial		HW9 out
Oct 30	Deep Reinforcement Learning I	policy gradient	slides exercise 9 solution 9
Nov 1	Deep Reinforcement Learning II	Q-learning actor-critic	slides		HW9 due 11:59pm solution1 solution2
Nov 2	Immitation learning		slides		HW10 out
Nov 6	Beyond RL I	gradient free optimization	slides exercise 10 solution 10
Nov 8	Beyond RL II	imitation learning dagger direct future prediction	slides		HW10 due 11:59pm solution
Nov 9	Gradient free optimization		slides		HW11 out
Nov 13	Embedding learning	triplet loss contrastive loss	slides exercise 11 solution 11
Nov 15	Style transfer	gram statistic	slides
Nov 16	Driving in SuperTuxKart				HW11 due 11:59pm Final project out
Nov 20	Language		slides
Nov 22	no class
Nov 23	no class
Nov 27	Adversarial examples	adversarial attack	slides	7.13
Nov 29	Negative results
Nov 30	Final project Q/A
Dec 4	Final project flash presentations				Final project due 11:59pm
Dec 6	Final project competition
Dec 7	Nagging TA for better grade				no class

concepts: layers/architecture algorithm supervision applications

Prerequisites

• 311 or 311H - Discrete math for computer science (or equivalent)
• 343 or 363D - Artificial Intelligence or Statistical Learning and Data Mining
• proficiency in Python
  • Bonus: numpy, scipy and matplotlib

Class overview

• The class meets three times a week, twice is a classroom setting, once in a lab setting
• In the classroom setting you’ll learn the theoretical and mathematical background on neural networks and deep learning
• In the lab you’ll learn to use tensorflow and implement the techniques learned in class

Goals of the class

After this class you should be able to

• Implement and train neural networks in tensorflow
• Have a basic understanding of the inner workings of neural networks
• Know several types of neural networks, including convolutional and recurrent neural networks

Grading

• 10% Quizzes (in class)
• 55% Homework
• 35% Final project

There will be in class quizzes throughout the semester (about one a week). You can work with your peers on those quizzes during class. Each quiz will be graded pass/fail.

There will be a homework assignment every week, but we will keep them small and manageable. Homework needs to be solved individually, if we detect duplicate solutions you will lose the full credit for the assignment. Most of the homework will be graded automatically, you’ll have access to a partial grader while you’re working on the assignment. We will use the same grader, but different test data for your final grade. You’ll submit your homework through canvas.

Late policy:

• 1 day: -25%
• 2 days: -50%
• 3+ days: -100%

Expected workload

Estimates of required effort to pass the class are:

• 3 hours per week: attend class
• 3 hours per week: background reading
• 3 hours per week: coding homework

Course material

The course should be self contained, but if you need additional reading material consult Deep learning, Goodfellow, Bengio and Courville 2016 www.deeplearningbook.org.

Notes

Syllabus subject to change.