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MonTue WedThuFri
Jan 25
Day 1: Welcome
Handout:
In-class Assignment: My first NN		 Jan 26 Jan 27
Day 2: Intro to ML: datasets, generalization, underfitting/overfitting
Preparation:Handout:
In-class Assignment: Colab/Python tutorial		 Jan 28
Quizzes available:
  • Greek
  • NumPy
Jan 29
Feb 01
Day 3: Hypothesis space, Linear Regression, Loss Functions
Preparation:In-class Assignment: You are the optimizer		 Feb 02 Feb 03
Day 4: Using the gradient to reduce the loss, Numerical differentiation
Preparation:In-class Assignment: Gradient descent from scratch		 Feb 04
Quizzes available:
  • Gradient Descent
Feb 05
Feb 08
Day 5: Stochastic Gradient Descent
Preparation:In-class Assignment: Symbolic and numeric differentiation		 Feb 09 Feb 10
Day 6: Regularization
Preparation:Handout:
In-class Assignment: Regularization and Weight Decay		 Feb 11
Quizzes available:
  • General ML
Feb 12
Feb 15
Day 7: Binary classification
Preparation:In-class Assignment: Binary Classification Functions		 Feb 16 Feb 17
Day 8: Optimizers, multi-class/multi-label
Preparation:Handout:
In-class Assignment: In-class assignment: Examining fastai source code		 Feb 18
Quizzes available:
  • Optimizers
Feb 19
Feb 22
Day 9: What is deep learning?
Preparation:Handout: Why NN can be universal function approximator
In-class Assignment: All-class exercise: forward propagation		 Feb 23 Feb 24
Day 10: Backpropagation, Vanishing/Exploding gradients
Preparation:Handout: Backpropagation.pdf
In-class Assignment: Backpropagation		 Feb 25 Feb 26
Mar 01
Day 11: K-fold cross-validation, embeddings
Preparation:		 Mar 02 Mar 03
Day 12: Regularization: Dropout & Batch Normalization; Bag of Words
Preparation:In-class Assignment: Implement Dropout		 Mar 04
Quizzes available:
  • Neural Networks
  • Activation Functions
  • Loss Functions
Mar 05
Mar 08
Spring Break		 Mar 09
Spring Break		 Mar 10
Spring Break		 Mar 11
Spring Break		 Mar 12
Spring Break
Mar 15
Day 13: Convolutions
Preparation:In-class Assignment: In-class assignment: convolutions		 Mar 16 Mar 17
Day 14: Convolutional Neural Networks (CNNs): mnist
Preparation:Handout: Otavio Good CNN Visualization YouTube video		 Mar 18
Quizzes available:
  • Regularization
Mar 19
Mar 22
Day 15: CNNs: Neural Style Transfer, Visualizing CNNs
Preparation:In-class Assignment: In-class assignment: VGG weights		 Mar 23 Mar 24
Day 16: Transfer learning, Multi-task learning, Generative Adversarial Networks (GANs), Cycle GANs
Preparation:In-class Assignment: In-class Assignment: transfer learning		 Mar 25
Quizzes available:
  • Transfer Learning
Mar 26
Mar 29
Day 17: CNNs: architectures and pre-trained
Preparation:		 Mar 30 Mar 31
Day 18: Optimize your Finanical Life
Preparation:In-class Assignment: In-class assignment: implementing Resnet and Inception Blocks		 Apr 01
Quizzes available:
  • CNNs
Apr 02
Apr 05
Day 19: Recurrent Neural Networks (RNNs) Intro
Preparation:		 Apr 06 Apr 07
Day 20: RNN implementation
Preparation:Handout: Getting money out of tax-advantage accounts video
In-class Assignment: In-class assignment: Implementing RNN		 Apr 08 Apr 09
Apr 12
Day 21: RNN: Backpropagation through time, Bidirectional RNN
Preparation:		 Apr 13 Apr 14
Day 22: RNN: LSTM and GRU
Preparation:Handout: GatedUnits.pdf
In-class Assignment: In-class assignment: using LSTM to implement XOR		 Apr 15 Apr 16
Apr 19
Day 23: RNN: Multilayer RNN, Generating sequences
Preparation:In-class Assignment: In-class assignment: LSTM Implementation		 Apr 20 Apr 21
Day 24: Attention: RNNs using Attention
Preparation:		 Apr 22
Quizzes available:
  • RNNs
Apr 23
Apr 26
Day 25: Attention: 2
Preparation:In-class Assignment: In-class assignment: Implementing RNNs with attention		 Apr 27 Apr 28
Day 26: No class today. There will be lab session, though.		 Apr 29
Quizzes available:
  • Attention:Transformers
Apr 30
May 03
Day 27: Transformers
Preparation:		 May 04 May 05
Day 28: Optimize your Engineering life
Preparation:		 May 06 May 07
May 10 May 11
Final period for students in Section 1 2-5PM: Retake any learning objective assessments as desired		 May 12 May 13
Final period for students in Section 2 2-5PM: Retake any learning objective assessments as desired

CS 152 home // Last updated Mon May 3 15:15:08 PDT 2021