From cb389cb4d03cf699b0380a8bc346fbc0cfc28bc0 Mon Sep 17 00:00:00 2001 From: Yue Zhao Date: Sat, 30 Nov 2024 22:48:48 -0800 Subject: [PATCH] minor update for 2025 Spring --- schedule.html | 360 +++++++++++++++++++++++++------------------------- 1 file changed, 178 insertions(+), 182 deletions(-) diff --git a/schedule.html b/schedule.html index 497f75e..13e1a25 100644 --- a/schedule.html +++ b/schedule.html @@ -104,189 +104,185 @@

Schedule

The schedule is subject to change : The course website is still under construction; please check back frequently.

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DateLectureHomework / ReadingsLogistics
Module 1: ML and DL Foundations
Week 1
Jan 12		 - 1. Course Introduction
- 2. Framing ML Problems
- 3. ML as Function Approximation
- 4. Linear Models
- 5. Shape the Course Together - 		S24-block1-intro-linear.pdf
Week 2
Jan 19		 - 0. Project idea discussion
- 1. Classical ML
-    - Decision Trees and Ensembles
-    - k-Nearest Neighbors
-    - Clustering
-    - Anomaly Detection
- - 2. Cloud computing service tutorial
- - 		S24-block2-classical-ML.pdf
Week 3
Jan 26		 - 1. Classical ML (continued)
- Neural Network Basics
-    - Perceptron Revisited
-    - Gradient Descent
-    - Forward Propagation
- 2. Project idea discussion
- 		S24-block3-NeuralNet.pdf
Week 4
Feb 2		 - 1. Neural Network Basics
-    - Backpropogation Propagation
-    - Vanishing Gradient
- 3. Different types of Neural Networks:
-    - Convolutional Neural Networks
- - 		Quiz 1Course Project Teams Formed; Pre-proposal DUE
Google Cloud Platform - 2024.pdf
Week 5
Feb 9		 - 1. Different types of Neural Networks:
- - Convolutional Neural Networks
- 2. Deep Learning Software Tutorial (maybe)
- 		Assignment 1 OUTS24-block4-CNN.pdf
S24-block6-GNN.pdf
Week 6
Feb 16		 - Different types of Neural Networks:
- 1. Recurrent Neural Networks (RNN) & LSTM
- 2. Graph Neural Networks (GNN)
- 		S24-block6-GNN.pdf
S24-block5-RNN-LSTM.pdf
Week 7
Feb 23		MIDTERM EXAM
Module 2: Deep Learning Applications & Advanced Topics
Week 8
Mar 1		 - Automated ML and Transfer Learning
- Guest Discussion: Open-source Development (Dr. Haifeng Jin @ Google) - 		S24-block7-Training-AutoML.pdf
Assignment 1 DUE
Week 9
Mar 8		Training dynamics
- Guest discussion: Dr. Souvik Kundu, Research Scientist @ Intel Labs (topic to be decided) - 		Assignment 2 OUT
Week 10
Mar 15		NO CLASS; Spring Recess
Week 11
Mar 22		 - Generative AI
- 1. Generative adversarial networks (GAN)
- 2. Variational AutoEncoder (VAE)
- 3. Case Study on Controllable Text Generation
- Guest Discussion: Valentino Constantinou, Senior Data Scientist, Team Lead @ Terran Orbital - 		Quiz 2S24-block8-GenAI-GAN-VAE.pdf
Course Project Mid-report DUE
Week 12
Mar 29		 - Attention, Relation, and Memory Networks
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DateLectureHomework / ReadingsLogistics
Module 1: ML and DL Foundations
Week 1
Jan 17		 + 1. Course Introduction
+ 2. My Research Overview: AI Robustness and Trustworthiness
+ 3. My Research Overview: AI4Science and ML Systems
+ 4. Framing ML Problems
+ 5. ML as Function Approximation
+ 6. Linear Models
+ 7. Job or Ph.D.? Is it a Question. +
Week 2
Jan 24		 + 1. Project idea discussion
+ 2. Classical ML
+    - Decision Trees and Ensembles
+    - k-Nearest Neighbors
+    - Clustering
+    - Anomaly Detection
+ 3. Cloud computing service tutorial
+
Week 3
Jan 31		 + 1. Classical ML (continued)
+ Neural Network Basics
+    - Perceptron Revisited
+    - Gradient Descent
+    - Forward Propagation
+ 2. Project idea discussion
+
Week 4
Feb 7		 + 1. Neural Network Basics
+    - Backpropagation
+    - Vanishing Gradient
+ 2. Different types of Neural Networks:
+    - Convolutional Neural Networks
+ 		Quiz 1Course Project Teams Formed; Pre-proposal DUE
Week 5
Feb 14		 + 1. Different types of Neural Networks:
+    - Convolutional Neural Networks
+ 2. Deep Learning Software Tutorial (maybe)
+ 		Assignment 1 OUT
Week 6
Feb 21		 + Different types of Neural Networks:
+ 1. Recurrent Neural Networks (RNN) & LSTM
+ 2. Graph Neural Networks (GNN)
+
Week 7
Feb 28		 + Automated ML and Transfer Learning
+ Guest Discussion (TBD) +
Week 8
Mar 7		 + Training dynamics
+ Guest Discussion (TBD) + 		Assignment 1 DUE
Week 9
Mar 14		MIDTERM EXAMAssignment 2 OUT
Week 10
Mar 21		NO CLASS; Spring Recess
Module 2: Deep Learning Applications & Advanced Topics
Week 11
Mar 28		 + Generative AI
+ 1. Generative adversarial networks (GAN)
+ 2. Variational AutoEncoder (VAE)
+ 3. Case Study on Controllable Text Generation
+ Guest Discussion (TBD) + 		Quiz 2Course Project Mid-report DUE
Week 12
Apr 4		 + Attention, Relation, and Memory Networks
+ Guest Discussion (TBD) + 		Assignment 2 DUE
Week 13
Apr 11		 + Contrastive Learning and Self-supervised Learning
+ Guest Discussion (TBD) +
Week 14
Apr 18		 + Reinforcement Learning
+ Guest Discussion (TBD) +
Week 15
Apr 25		Team Project Presentations (zoom; TBA)
Week 16
May 2		Team Project Presentations (in person)
Final ReportFinal Report Due on University Final Exam Day(No in-class Exam)Final Project Report due
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- Guest Discussion: Prof. Andrei Irimia, Neural Networks Applications in Healthcare - 		Assignment 2 DUE
S24-block9-Attention-relational-memory.pdf
Week 13
Apr 5		 - Contrastive Learning and Self-supervised Learning
- 		S24-block10-SSL-Diffusion-More.pdf
Week 14
Apr 12		Reinforcement Learning
- Guest discussion: Howie Xu, SVP Engineering AI/ML @ Palo Alto Networks		S24-block11-RL.pdf
Week 15
Apr 19		Team Project Presentations (zoom; tba)
Week 16
Apr 26		Team Project Presentations (in person)
Final ReportFinal Report Due on University Final Exam Day, Wed May 3(No in-class Exam)Final Project Report due -
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