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CS 490/590 - Intro to Neural Networks and Deep Learning
2024 Spring
An introduction to the basic ideas and techniques underlying deep neural networks and machine learning.
Table of contents
- UNIT 1: Introduction to Deep Learning and Machine Learning
- UNIT 2: Fundamentals of Neural Networks
- UNIT 3: Applications of Deep Learning
Tentative schedule is provided below – assignments dates and lectures will be updated as the course progresses.
UNIT 1: Introduction to Deep Learning and Machine Learning
UNIT 2: Fundamentals of Neural Networks
UNIT 3: Applications of Deep Learning
| WEEK 10 |
| L10: Convolutional Neural Networks L10: CNN Details L11: Image Classification |
| R10: CNNs R11: CNNs Continued |
| T4: Optimization & CNNsExtra Tutorial: How to Train NNetsT5: CNN Details |
| V13: Edge Detection V14: Padding V15: Strided Convolutions |
| V16: 1 Layer Convolutions V17: Pooling V18: Full CNN |
| WEEK 11 |
| OLD EXAMS: i) 2014, ii) 2015a(soln), 2015b(soln), iii) 2017a, 2017b, a & b solns, iv) 2018a, 2018b, a & b solns |
| MIDTERM EXAM on (Thu 3/21) |
| WEEK 12 |
| Midpoint Presentations |
| Proj Check-in (Due: Tue., 04/09 - Week 14) |
| WEEK 13 |
| L12: Recurrent Neural Nets |
| R12: RNNs R13: LSTMs |
| T6: RNNs |
| V19: Distributed RepresentationsV20: Training RNNsV21: RNN Toy Example |
| V22: RNN GradientsV23: LSTMs |
| WEEK 14 |
| L13: Transformers |
| L14: Learning Probabilistic Models L15: Generative Adversarial Networks |
| R14: Learning Probabilistic Models R15: Generative Adversarial Networks |
| Check-in Presentations |
| Proj Final Presentation (Due: Tue-Thu., 04/23 & 04/25 - Week 16) |
| WEEK 15 |
| L16: VAEs |
| R16: Auto-Encoding Variational Bayes |
| T7: VAEs |
| V24: Kingma: Auto-Encoding Variational Bayes |
| L17: RL |
| WEEK 16 |
| Final Presentation |
| WEEK 17 |
| FINALS WEEK |
| FINAL EXAM: 8:00am - 9:40am, Thursday, May 2, 2024, SE 2214 |
| Final Exam Samples: 2011, 2012, 2013, 2016, 2017 & solution, 2018 & solution |