intelligence lecture

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1. 1▶Reliable and Interpretable Artificial Intelligence -- Lecture 12 (Randomized Smoothing)Reliable and Interpretable Artificial Intelligence (2020)Notes
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2. 2▶Reliable and Interpretable Artificial Intelligence -- Lecture 11 (Combining Deep Learning and Logic)Reliable and Interpretable Artificial Intelligence (2020)Notes
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3. 3▶Reliable and Interpretable Artificial Intelligence -- Lecture 10 (Visualization)Reliable and Interpretable Artificial Intelligence (2020)Notes
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4. 4▶Reliable and Interpretable Artificial Intelligence -- Lecture 9 (Geometric Robustness)Reliable and Interpretable Artificial Intelligence (2020)Notes
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5. 5▶Reliable and Interpretable Artificial Intelligence -- Lecture 8 (Certified Defenses)Reliable and Interpretable Artificial Intelligence (2020)Notes
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6. 6▶Reliable and Interpretable Artificial Intelligence -- Lecture 7 (DeepPoly Relaxation)Reliable and Interpretable Artificial Intelligence (2020)Notes
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7. 7▶Reliable and Interpretable Artificial Intelligence -- Lecture 6 (Zonotope Relaxation)Reliable and Interpretable Artificial Intelligence (2020)Notes
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8. 8▶Reliable and Interpretable Artificial Intelligence -- Lecture 5 (Complete Certification)Reliable and Interpretable Artificial Intelligence (2020)Notes
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9. 9▶Reliable and Interpretable Artificial Intelligence -- Lecture 4b (Certification of Neural Networks)Reliable and Interpretable Artificial Intelligence (2020)Notes
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10. 10▶Reliable and Interpretable Artificial Intelligence -- Lecture 4a (Adversarial Defenses)Reliable and Interpretable Artificial Intelligence (2020)Notes
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11. 11▶Machine Intelligence - Lecture 14 (Overfitting in Deep Learning, Reinforcement Learning)SYDE 522 – Machine Intelligence (Winter 2018, University of Waterloo)Notes
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12. 12▶Machine Intelligence - Lecture 13 (Convolutional Neural Networks, CNNs)SYDE 522 – Machine Intelligence (Winter 2018, University of Waterloo)Notes
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13. 13▶Machine Intelligence - Lecture 12 (Problems of Learning, RBMs, Autoencoders)SYDE 522 – Machine Intelligence (Winter 2018, University of Waterloo)Notes
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14. 14▶Machine Intelligence - Lecture 11 (Backpropagation, Topology, Overfitting, Autoencoders)SYDE 522 – Machine Intelligence (Winter 2018, University of Waterloo)Notes
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15. 15▶Machine Intelligence - Lecture 10 (Regression, Neurons, Perceptron, Learning)SYDE 522 – Machine Intelligence (Winter 2018, University of Waterloo)Notes
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16. 16▶Machine Intelligence - Lecture 9 (Cluster Validity, Probability, Fuzzy Sets, FCM)SYDE 522 – Machine Intelligence (Winter 2018, University of Waterloo)Notes
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17. 17▶Machine Intelligence - Lecture 8 (SOM learning, Support Vector Machines)SYDE 522 – Machine Intelligence (Winter 2018, University of Waterloo)Notes
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18. 18▶Machine Intelligence - Lecture 7 (Clustering, k-means, SOM)SYDE 522 – Machine Intelligence (Winter 2018, University of Waterloo)Notes
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19. 19▶Machine Intelligence - Lecture 6 (Validation, Overfitting, Underfitting)SYDE 522 – Machine Intelligence (Winter 2018, University of Waterloo)Notes
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20. 20▶Machine Intelligence - Lecture 5 (Computer Vision, Features, Fisher Vector, VLAD)SYDE 522 – Machine Intelligence (Winter 2018, University of Waterloo)Notes
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