Although I wrote this late - as in the exam is already over - still I want to write down outlines of all my courses.

So here is the contents of the course ENGSCI 213, "Mathematical Modelling 2 SE" (i.e. "for software engineering").

linear algebra
	Gaussian Elimination
		row operations / 
		pivot position
			pivot column
			pivot variable
			free variable
		row reduced echolon form
			elementary matrix
			permutation matrix
	trivial solution
	linear combination
	linear independence
	invertible matrix
	LU factorization
		partial pivoting
			* rook pivoting
			* full pivoting
		linear transformation
			translation & homogeneous
		column space of a matrix
		null space of a matrix
			coordinate vector (of a point/vector) relative to a basis
		dimension (of a subspace)
		rank (of a matrix)
			 invertible matrices
		~ of triangular matrices
		~ and row operations
		* the proportional change of the "area" of the "unit cube"
		(an) eigenvector (corresponding to an eigenvalue)
		diagnolizable matrices
		the Power Method
			the Inverse Power Method
	affine combination
		affine span
		standard homogeneous form
		affinely dependent
		barycentric coordinates
	iterative methods for linear systems of equations
		Jacobi Method (simultaneous corrections)
			matrix form
		Gauss-Seidel Method (sequential corrections)
			matrix form
		convergence considerations
			Frobenius Norm
data analysis
	using R
	mean, standard deviation, variance
	centre, spread, (!!) skewness
	confidence interval
		standard error
	!! hypothesis testing (general)
		null hypothesis / alternative hypothesis
		"test statistic"
	(testing normality)
		normal quantile-quantile plot
		Shapiro-Wilk Test
			its hypothesis
	Central Limit Theorum (just interpreting it)
	transforming the data by taking logarithm
		intention on normality
		intention on equal variance
		assumptions & hypotheses
		"one-sample t-test"
		two-sample t-tests
			variance-equality considerations
			"Welch Two Sample t-test"
	Levene Test
		its hypothesis
		interpreting R output
	One-way analysis of variance
		assumptions & hypotheses
		why it works
			(intending to understand the "ANOVA Table")
			degrees of freedom
		Tukey Intervals vs. Bonferroni Correction
		Frequentist View vs. Subjectivist View
		Sample Space
			(Null Event)
		(n) Mutually Exclusive Events
			partitioning a sample space
			partitioning an event
		Probability Distribution
			Discrete ~
		Partition Theorum
		Bayes' Theorum (up to the "extended form")
		Statistical Independence
			mutually independent
	Discrete Probability Distributions
		Probability Mass Function
		Binomial D
			Bernoulli Trial
		Poisson D
			~ Poission Process
		Expected Value
			linear combination
			linear combination
	Continuous Probability Distributions
		Probability Function (density)
		Distribution Function (cumulative)

		Uniform Distribution
			properties / parameters
		Exponential Distribution
			'memoryless' property (e.g. `P(X>=30|X>=20)==P(X>=10)`)
			relationship with Poisson Distribution
		Normal Distribution
			~ nature, perhaps from Central Limit Theorum
Markov Processes
	Stochaistic Process
		sample space
		state space
			finite ~
			stationary ~
				finite ~
				infinite ~
		Markov Property
			Markov Process / Markov Chain
			transition probability
				"one step transition probabilities"
				transition matrices `P[t]` (for period t)
				"n step transition probabilites"
				stationary ~
			"value", `v[i,t]`, of being in state i during period t.
			"total expected value for starting in state `i` over `t` periods", `V[i,t]`
			Limiting Distribution
			Stopping Problem