CPT_S 434/534 Neural Network Design and Application

Spring 2025

Class info

Time: 7:45AM - 9AM (Good times:))

Date: Tue/Thu

Location: CLEV 30

Materials

Textbooks:

Lecture slides: see the lecture table below

Assignments: please check Canvas

Instructor office hours

Time: 11:30AM-12:30PM

Date: Tue

Location: EME 123

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Office hour time:

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(Web conferencing details provided upon confirmation)

Guest TA (Technical Consultation for Course Projects)

Office hour time: 11:30AM - 12:30PM

Office hour date: every other Tuesday (starting from 1/14, except 2/25 and 3/11)

Office: Dana 114

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Lectures

Date (m/d)	Lecture	Topic	Materials (PDF)
1/7	1	Syllabus	Syllabus
1/14	2	[ML] ML basics	Slides
1/16	3	[ML] PAC learning	Slides
1/21	4	[ML] ERM and MLE	Slides
1/28	5	[NN] NN basics	Slides
1/30	6	[NN] Compositing units in NN	Slides
2/4	7	[NN] MLP Softmax classifier	Slides
2/11	8	[CNN] Convolutional layer and CNN	Slides
2/13	9	[CNN] CNN architectures	Slides
2/18	10	[CNN] Practical training of CNN	Slides
2/25	11	[RNN] Sequence data and RNN	Slides
3/4	12	[RNN] LSTM and RNN architectures	Slides
3/6	13	[RNN] Attention and transformers	Slides
3/18	14	[GNN] Graph data and representation	Slides
3/20	15	[GNN] GNN	Slides
4/1	16	[GNN] Oversmoothing	Slides
4/3	17	[GAN] Adversarial ML	Slides
4/10	18	[GAN] Generative model and GAN	Slides
4/15	19	[GAN] Practical GAN training	Slides

TA Sessions

Readings (see Syllabus for details)

Section	Book Chapter or Paper
[ML]	Book “Foundations of machine learning”: Chapter 1
[ML]	Book “Foundations of machine learning”: Chapter 2
[ML]	Book “Foundations of machine learning”: Chapter 3
[ML]	Book “Foundations of machine learning”: Chapter 4.1, 4.2
[ML]	Book “Deep learning”: Chapter 5
[NN]	Book “Deep learning”: Chapter 6
[CNN]	Book “Deep learning”: Chapter 9
[CNN]	Book “Deep learning”: Chapter 7.1-7.4, 7.12
[CNN]	Book “Dive into deep learning”: Chapter 4.5, 4.6, 13.1
[CNN]	Paper Alexnet
[CNN]	Paper Spatial pyramid networks
[CNN]	Paper Network in network
[CNN]	Paper Inception
[CNN]	Paper ResNet
[RNN]	Book “Deep learning”, Chapter 10.1-10.5, 10.7, 10.10
[RNN]	Paper Transformer