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CS480/680 Lecture 9: Perceptrons and single layer neural nets
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Classroom Contents
Introduction to Machine Learning - Spring 2019 - University of Waterloo
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- 1 CS480/680 Lecture 1: Course Introduction
- 2 CS480/680 Lecture 2: K-nearest neighbours
- 3 CS480/680 Lecture 3: Linear Regression
- 4 CS480/680 Lecture 4: Statistical Learning
- 5 CS480/680 Lecture 5: Statistical Linear Regression
- 6 CS480/680 Lecture 6: Tools for surveys (Paulo Pacheco)
- 7 CS480/680 Lecture 6: Kaggle datasets and competitions
- 8 CS480/680 Lecture 6: Normalizing flows (Priyank Jaini)
- 9 CS480/680 Lecture 6: Unsupervised word translation (Kira Selby)
- 10 CS480/680 Lecture 6: Fact checking and reinforcement learning (Vik Goel)
- 11 CS480/680 Lecture 6: Sum-product networks (Pranav Subramani)
- 12 CS480/680 Lecture 6: EM and mixture models (Guojun Zhang)
- 13 CS480/680 Lecture 6: Model compression for NLP (Ashutosh Adhikari)
- 14 CS480/680 Lecture 7: Mixture of Gaussians
- 15 CS480/680 Lecture 8: Logistic regression and generalized linear models
- 16 CS480/680 Lecture 9: Perceptrons and single layer neural nets
- 17 CS480/680 Lecture 10: Multi-layer neural networks and backpropagation
- 18 CS480/680 Lecture 11: Kernel Methods
- 19 CS480/680 Lecture 12: Gaussian Processes
- 20 CS480/680 Lecture 13: Support vector machines
- 21 CS480/680 Lecture 14: Support vector machines (continued)
- 22 CS480/680 Lecture 15: Deep neural networks
- 23 CS480/680 Lecture 16: Convolutional neural networks
- 24 CS480/680 Lecture 17: Hidden Markov Models
- 25 CS480/680 Lecture 18: Recurrent and recursive neural networks
- 26 CS480/680 Lecture 19: Attention and Transformer Networks
- 27 CS480/680 Lecture 20: Autoencoders
- 28 CS480/680 Lecture 21: Generative networks (variational autoencoders and GANs)
- 29 CS480/680 Lecture 22: Ensemble learning (bagging and boosting)
- 30 CS480/680 Lecture 23: Normalizing flows (Priyank Jaini)
- 31 CS480/680 Lecture 24: Gradient boosting, bagging, decision forests
