Classes start on January 15, 2019

Date Topic Reading Notes
Tue, Jan 15 Course Overview
CMU Computing Policy
CMU Policy on Academic Integrity
Nature Review on Deep Learning
Classification Intro
PART I: Machine Learning Basics
Thur, Jan 17 Intro to Machine Learning I
(Linear Algebra Review, Logistic Regression)
Deep Learning Book, CH2
Linear Classification Basics
Tue, Jan 22 Intro to Machine Learning II
(Gradient Descent, Stochastic Gradient Descent))
HW1 Out:
Stochastic Gradient Descent
Deep Learning Book, CH3
Thur, Jan 24 Deep Learning Software
Documentation of Keras
Documentation of Tensorflow
PART II: Intro to Deep Learning
Tue, Jan 29 Back Propagation Back Propagation
Thur, Jan 31 Deep Feedforward Networks
Deep Learning Book, CH6
Setting up the Data and Model
Tue, Feb 5 DFN Training
HW1 Due (before class)
Deep Learning Book, CH6
Learning and Evaluation
Thur, Feb 7 DFN Training
HW2 Out
Deep Learning Book, CH6
Learning and Evaluation
Tue, Feb 12 Convolutional Neural Networks I
HW2 Tutorial
CNN Architecture
Deep Learning Book, CH9
Thur, Feb 14 Convolutional Neural Networks II CNN Visualization
Deep Learning Book, CH9
PART III: Explanation for Deep Neural Networks
Tue, Feb 19 Paper Discussion: Influence-Directed Explanations for CNNs Influence-Directed Explanations for CNNs Slides
Thur, Feb 21 Paper Discussion: Feature-Wise Bias Amplifications
HW3 out Friday Feb 22
Feature-Wise Bias Amplifications Slides
Tue, Feb 26 Paper Discussion: Feature-Wise Bias Amplifications
Feature-Wise Bias Amplifications
optional: Understanding Black-box Predictions via Influence Functions
Thur, Feb 28 Paper Discussion: Chih-Kuan Yeh & Joon Sik Kim Representer point selection for DNN Slides
Tue, Mar 5 Paper Discussion: Been Kim Quantitative Testing with Concept Activation Vectors (TCAV)
Sanity Checks for Saliency Maps
An Evaluation of the Human-Interpretability of Explanation
PART IV: Adversarial Learning
Thur, Mar 7 Paper Discussion: Adversarial Settings in Deep Learning
The Limitations of DL in Adversarial Settings Slides
Tue, Mar 12 No Class: Spring Break
Thur, Mar 14 No Class: Spring Break
Tue, Mar 19 Paper Discussion: Nicolas Carlini
Towards Evaluating the Robustness of Neural Networks Slides
Thur, Mar 21 Paper Discussion: Mahmood Sharif
HW 3 Due before class
HW 4 Part I Out
DReal and Stealthy Attacks on State-of-the-Art Face Recognition
Face Recognition and Privacy
Slide1 Slide2
Tue, Mar 26 Overview of Privacy Attacks Reference Papers at the end of the slide Slide
Thur, Mar 28 Paper Discussion: Whitebox Membership Inference TBD Slide
PART V: Fairness and Recurrent Neural Networks
Tue, Apr 2 Overview of Fairness in DL: Emily Black (Guest Lectuerer) Mitigating Unwanted Biases with Adversarial Learning Slide
Thur, Apr 4 Fairness in DL: Emily Black (Guest Lectuerer) Papers in the reference slide Slide
Tue, Apr 9 Word Embeddings word2vec Slide
Thur, Apr 11 CMU Carnival: No class
HW 4 Due (before 10:30 AM EST)
Tue,Apr 16 Paper Discussion: Bias In Word Embeddings Man is to Computer Programmer as Woman is to Homemaker?
Human-like Bias in Langauge Models
Thur, Apr 18 Recurrent Neural Networks I
HW 5 Part I Out
Deep Learning Book, CH10 Slide
Tue, Apr 23 Recurrent Neural Networks II Deep Learning Book]: Sequence Modeling: Recurrent and Recursive Neural Nets(Sections 10.1 and 10.2)
CH10 N-gram Language Models (textbook chapter)
The Unreasonable Effectiveness of Recurrent Neural Networks(blog post overview)
Thur, Apr 25 Bias in NLP tasks
HW 5 Part II Out
Bias in NLP tasks Slide
Thur, Apr 25 Recurrent Neural Networks III: LSTM Deep Learning Book: Sequence Modeling: Recurrent and Recursive Neural Nets(Sections 10.3, 10.5, 10.7-10.12)
Learning long-term dependencies with gradient descent is difficult (one of the original vanishing gradient papers)
Exploding and Vanishing Gradients
Understanding LSTMs
Slide2 Slide3
Thur, May 2 Class Wrapup
HW 5 Due May 7th 1:30PM EST

* Schedule is subject to change.