Fun­da­men­tals of Big Da­ta An­a­lyt­ics

Lec­ture Con­tents

  1. Ma­trix Al­ge­bra and Ex­tremals of Ma­trix Func­tions
  2. Mul­ti­vari­ate Anal­y­sis and Ran­dom Vec­tors
  3. Di­men­sion­al­i­ty Re­duc­tion Meth­ods
    1. Prin­ci­pal Com­po­nent Anal­y­sis
    2. Mul­ti­di­men­sion­al Scal­ing
    3. IsoMap
    4. Dif­fu­sion Map
  4. Clas­si­fi­ca­tion and Clus­ter Anal­y­sis
    1. Lin­ear Dis­crim­i­nant Anal­y­sis
    2. Max­i­mum A-pos­te­ri­ori Prob­a­bil­i­ty (MAP) Clas­si­fi­er
    3. Maximum Likelihood (ML) Classifier
    4. k-Means
    5. Spec­tral Clus­ter­ing
    6. Hi­er­ar­chi­cal Clus­ter­ing
  5. Sup­port-Vec­tor Ma­chines
    1. Con­vex Op­ti­miza­tion The­o­ry
    2. Sup­port-Vec­tor Ma­chine (SVM) Al­go­rithm
    3. Se­quen­tial Min­i­mal Op­ti­miza­tion (SMO) Al­go­rithm
    4. Ker­nel SVM
  6. Re­gres­sion
    1. lin­ear
    2. lo­gis­tic
    3. per­cep­tron


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Last edit­ed: 2020-08-26 12:20:21