machine intelligence lecture

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1. 1▶Machine Intelligence - Lecture 14 (Overfitting in Deep Learning, Reinforcement Learning)SYDE 522 – Machine Intelligence (Winter 2018, University of Waterloo)Notes
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2. 2▶Machine Intelligence - Lecture 13 (Convolutional Neural Networks, CNNs)SYDE 522 – Machine Intelligence (Winter 2018, University of Waterloo)Notes
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3. 3▶Machine Intelligence - Lecture 12 (Problems of Learning, RBMs, Autoencoders)SYDE 522 – Machine Intelligence (Winter 2018, University of Waterloo)Notes
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4. 4▶Machine Intelligence - Lecture 11 (Backpropagation, Topology, Overfitting, Autoencoders)SYDE 522 – Machine Intelligence (Winter 2018, University of Waterloo)Notes
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5. 5▶Machine Intelligence - Lecture 10 (Regression, Neurons, Perceptron, Learning)SYDE 522 – Machine Intelligence (Winter 2018, University of Waterloo)Notes
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6. 6▶Machine Intelligence - Lecture 9 (Cluster Validity, Probability, Fuzzy Sets, FCM)SYDE 522 – Machine Intelligence (Winter 2018, University of Waterloo)Notes
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7. 7▶Machine Intelligence - Lecture 8 (SOM learning, Support Vector Machines)SYDE 522 – Machine Intelligence (Winter 2018, University of Waterloo)Notes
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8. 8▶Machine Intelligence - Lecture 7 (Clustering, k-means, SOM)SYDE 522 – Machine Intelligence (Winter 2018, University of Waterloo)Notes
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9. 9▶Machine Intelligence - Lecture 6 (Validation, Overfitting, Underfitting)SYDE 522 – Machine Intelligence (Winter 2018, University of Waterloo)Notes
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10. 10▶Machine Intelligence - Lecture 5 (Computer Vision, Features, Fisher Vector, VLAD)SYDE 522 – Machine Intelligence (Winter 2018, University of Waterloo)Notes
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