In addition to the lectures and notes provided in lectures tab, you will find relevant topics discussed in great details in the following materials:
Prerequisite understandings / readings for soft computing:
Understanding of the following self-study materials before the course:
-
Calculus:
-
Functions; Graph of a Function; Maxima and Minima; Continuity
-
Derivatives; Differentiability; The Differentiation of Composite Functions; Rules for Differentiation (chain rule); Derivatives of Elementary Functions
-
Functions of Two or More Variables; Continuity; Partial Derivatives; Differentiation of Composite Functions
-
Linear Algebra:
-
Vectors and Matrices; Matrix operations
-
Systems of Linear Equations
-
Vector Spaces
-
Inner Product Spaces; Orthogonality
-
Determinants
-
Diagonalization; Eigenvalues and Eigenvectors
-
Statistics:
-
Descriptive statistics
-
Sets and counting
-
Basic probability
-
Conditional probability and independence
-
Random variables and Probability distributions
-
Binomial and Normal distributions
-
Data Structure and Algorithms:
-
Linear data structures: array, lists, linked lists, stack, queue.
-
Non-Linear data strucures: tress and graphs.
-
Searching, sorting, hashing and recursion.
-
Divide and conquer, greedy and dynamic programming approaches
-
NP-Complete algorithms
-
Time and space complexity