Artificial Neural Network

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Lecture 1: What are neural networks?
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Reading: How neural networks learn from experience ( .pdf)

Lecture 2: Two simple learning algorithms
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Reading: Connectionist Learning Procedures, pp 185-190; 193-198. ( .pdf)

Lecture 3: Learning in multilayer networks
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Lecture 4: Learning to model relationships and word sequences
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Reading: A neural probabilistic language model. (.ps file)

Lecture 5: Applying backpropagation to shape recognition
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Lecture 6: Learning in recurrent networks
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Reading: Learning internal representations by error propagation, pp 354-362. ( .pdf)


Lecture 8: The Bayesian way to fit models
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Lecture 9: More on Bayesian model fitting
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Lecture 10: Speeding up learning
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Lecture 11: Learning without a teacher: Autoencoders and PCA
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Lecture 12: Clustering: The EM algorithm for fitting mixtures of Gaussians
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Lecture 13: Mixtures of experts
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Reading: Adaptive mixtures of local experts .pdf 

Lecture 14: Hidden Markov Models
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Reading: Alan Poritz, Hidden Markov Models: A guided tour., ICASSP 1988. pdf

Lecture 15 (called 17): Learning Hidden Markov Models using EM
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Reading: Alan Poritz, Hidden Markov Models: A guided tour., ICASSP 1988. .pdf

Lecture 16: Distributed representations
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Reading: Book chapter on "Distributed Representations" .pdf 

Lecture 17: The effects of hardware damage on distributed representations
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Reading: Scientific American article on "Simulating Brain Damage" .pdf 

Lecture 18: Hopfield Nets and simulated annealing
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Reading: For a gentle introduction to the idea of memories as energy minima ( .ps ) ( .html )
Reading: For a gentle introduction to how to add new memories by creating new minima ( .ps ) ( .html 

Lecture 19: Boltzmann machines as probabilistic models
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Lecture 20: Learning in Boltzmann machines
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Reading: Scholarpedia entry on Boltzmann machines .pdf [ web page

Lecture 21: Some demonstrations of learning in restricted Boltzmann machines
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Lecture 22: Learning features one layer at a time
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Reading for lectures 22 and 23: "Learning multiple layers of representation". .pdf

Lecture 23: Sigmoid belief nets and the wake-sleep algorithm
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Reading for lectures 22 and 23: "Learning multiple layers of representation". .pdf 

Lecture 24: Using backpropagation to fine-tune deep networks
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Reading for lecture 24: "Reducing the dimensionality of data with neural networks" .pdf 

Lecture 25: Learning distributed representations for sequential data.

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