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  1. .gitattributes +10 -0
  2. ITPRLA/Lecture 10： An Introduction To Bayesian Inference (II)： Inference Of Parameters And Models [mDVE0M-xQlc].mp4 +3 -0
  3. ITPRLA/Lecture 11： Approximating Probability Distributions (I)： Clustering As An Example Inference Problem [XJGfXuFQVNE].mkv +3 -0
  4. ITPRLA/Lecture 12： Approximating Probability Distributions (II)： Monte Carlo Methods (I) [sN_0iGWcyLI].mp4 +3 -0
  5. ITPRLA/Lecture 13： Approximating Probability Distributions (III)： Monte Carlo Methods (II)： Slice Sampling [Qr6tg9oLGTA].mkv +3 -0
  6. ITPRLA/Lecture 14： Approximating Probability Distributions (IV)： Variational Methods [rkV6Wu30x4g].mp4 +3 -0
  7. ITPRLA/Lecture 15： Data Modelling With Neural Networks (I)： Feedforward Networks： The Capacity Of A Neuron [Z1pcTxvCOgw].mkv +3 -0
  8. ITPRLA/Lecture 16： Data Modelling With Neural Networks (II)： Content-Addressable Memories And State [OvMGPHpa_tM].mkv +3 -0
  9. ITPRLA/Lecture 1： Introduction to Information Theory [BCiZc0n6COY].mkv +3 -0
  10. ITPRLA/Lecture 2： Entropy and Data Compression (I)： Introduction to Compression, Inf.Theory and Entropy [y5VdtQSqiAI].mp4 +3 -0
  11. ITPRLA/Lecture 3： Entropy and Data Compression (II)： Shannon's Source Coding Theorem, The Bent Coin Lottery [0SxJl5G2bp0].mkv +3 -0
  12. ITPRLA/Lecture 4： Entropy and Data Compression (III)： Shannon's Source Coding Theorem, Symbol Codes [eHGqNvkL4n4].mp4 +3 -0
  13. ITPRLA/Lecture 5： Entropy and Data Compression (IV)： Shannon's Source Coding Theorem, Symbol Codes [cJ_rhZ9DP9k].mkv +3 -0
  14. ITPRLA/Lecture 6： Noisy Channel Coding (I)： Inference and Information Measures for Noisy Channels [9w4LnXIip5A].mkv +3 -0
  15. ITPRLA/Lecture 7： Noisy Channel Coding (II)： The Capacity of a Noisy Channel [vVAsh5DAe10].mp4 +3 -0
  16. ITPRLA/Lecture 8： Noisy Channel Coding (III)： The Noisy-Channel Coding Theorem [KSV8KnF38bs].mkv +3 -0
  17. ITPRLA/Lecture 9： A Noisy Channel Coding Gem, And An Introduction To Bayesian Inference (I) [HrRNqb5C-b0].mkv +3 -0
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