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 
Slides 
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 
Slides 
Tue, Jan 22  Intro to Machine Learning II (Gradient Descent, Stochastic Gradient Descent)) HW1 Out: 
Stochastic Gradient Descent Deep Learning Book, CH3 
Slides 
Thur, Jan 24  Deep Learning Software 
Documentation of Keras Documentation of Tensorflow 
Slides 
PART II: Intro to Deep Learning  
Tue, Jan 29  Back Propagation 
Back Propagation 
Slides 
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 
Slides 
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 
Slides 
Thur, Feb 14  Convolutional Neural Networks II 
CNN Visualization Deep Learning Book, CH9 
Slides 
PART III: Explanation for Deep Neural Networks  
Tue, Feb 19  Paper Discussion: InfluenceDirected Explanations for CNNs  InfluenceDirected Explanations for CNNs  Slides 
Thur, Feb 21  Paper Discussion: FeatureWise Bias Amplifications HW3 out Friday Feb 22  FeatureWise Bias Amplifications  Slides 
Tue, Feb 26 
Paper Discussion: FeatureWise Bias Amplifications 
FeatureWise Bias Amplifications optional: Understanding Blackbox Predictions via Influence Functions 

Thur, Feb 28  Paper Discussion: ChihKuan 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 HumanInterpretability of Explanation 
Slides 
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 StateoftheArt 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? Humanlike Bias in Langauge Models 
Slide 
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 Ngram Language Models (textbook chapter) The Unreasonable Effectiveness of Recurrent Neural Networks(blog post overview) 
Slide 
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, 10.710.12) Learning longterm dependencies with gradient descent is difficult (one of the original vanishing gradient papers) Exploding and Vanishing Gradients Understanding LSTMs 
Slide1 Slide2 Slide3 
Thur, May 2 
Class Wrapup HW 5 Due May 7th 1:30PM EST 
TBD 
* Schedule is subject to change.