In­for­ma­tion The­o­ry

Lec­ture Con­tents

  1. Fun­da­men­tals of In­for­ma­tion The­o­ry for Dis­crete Ran­dom Vari­ables
    1. In­for­ma­tion Mea­sures
    2. In­equal­i­ties
    3. In­for­ma­tion Mea­sures for Ran­dom Se­quences
    4. Asymp­tot­ic Equipar­ti­tion Prop­er­ty (AEP)
  2. Fun­da­men­tals of In­for­ma­tion The­o­ry for Con­tin­u­ous Ran­dom Vari­ables
  3. Source Cod­ing
    1. Clas­si­fi­ca­tion of Codes
    2. Bounds on Code­word Length and Ex­is­tence of Codes
    3. Huffman Cod­ing
    4. Arith­metic Cod­ing
  4. Com­mu­ni­ca­tion Chan­nels and In­for­ma­tion Ca­pac­i­ty
    1. Dis­crete Chan­nel Mod­els
    2. Chan­nel Ca­pac­i­ty
    3. Lin­ear Chan­nel Mod­els
    4. Ca­pac­i­ty of Fun­da­men­tal Chan­nels
    5. Chan­nel Cod­ing
    6. De­cod­ing Rules
    7. Er­ror Prob­a­bil­i­ties
  5. Rate Dis­tor­tion The­o­ry
  6. Ma­chine Learn­ing and Bi­o­log­i­cal In­for­ma­tion Ex­change


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Last edit­ed: 2020-08-26 12:20:21