parkerjgit
2/2/2018 - 11:36 PM

.gitignore

Math resources

tbd...


Calculus and Algebra Refreshers

description...

coursework:

-none-

supplementary:

vectors matrices https://www.khanacademy.org/math/linear-algebra https://www.khanacademy.org/math/precalculus

reference:

Discrete Math

description...

coursework
No.CourseInstitutionEffortStatus
xxxDiscrete MathematicsMIT / OCW
xxxComputational Probability and InferenceMIT/edx
supplementary (tutorials, etc)

xxx | Introduction to Probability - The Science of Uncertainty | MIT/edx || https://www.khanacademy.org/math/precalculus/prob-comb https://www.khanacademy.org/math/algebra/sequences https://www.khanacademy.org/math/integral-calculus/series-ic Bit Manipulation Set Notation

key texts:
see also:

xxx | Combinatorial Analysis | MIT/ocw || xxx | Algebraic Combinatorics | MIT/ocw || xxx | Geometric Combinatorics | MIT/ocw ||


unsortted

http://atlv.org/education/geometry/

Math Refresh

concepts
  • Discrete Math
    • proofs
    • Induction
    • Strong induction
    • Number theory
    • Graph theory and coloring
    • Matching problems
    • Graph theory II: minimum spanning trees
    • Communication networks
    • Graph theory III
    • Relations, partial orders, and scheduling
    • Sums and asymptotics
    • Divide and conquer recurrences
    • Linear recurrences
    • Counting

    • Probability introduction
    • Conditional probability
    • Independence
    • Random variables
    • Expectation
    • Large deviations
    • Random walks
  • Discrete Probability
    • discrete probability
    • random variables and events
      • Simpson’s paradox
      • Monty Hall
      • boy or girl paradox
    • Inference, and structure in distributions
      • The product rule
  • Inference in Graphical Models
    • Efficiency in Computer Programs
    • Graphical Models
    • Inference in Graphical Models - Marginalization
    • Marginalization in Hidden Markov Models
    • Robot Localization
    • Inference with Graphical Models
    • MAP Estimation in Hidden Markov Models
  • Learning Probabilistic Models
    • Introduction to Learning Probabilistic Models
    • Parameter Learning
      • Maximum Likelihood and MAP Estimation
      • Parameter Learning - Naive Bayes Classification
    • Structure Learning
      • Trees