Week 12: Full Course Synthesis & Exam Revision Map
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Comprehensive course-wide compression layer: algebra, calculus, discrete math, formula cheat sheets, and high-frequency traps. # Mathematics I — Week 12: Full Course Synthesis & Exam Revision Map The IITM syllabus lists **Week 12 as revision**, so there is no new topic to introduce here.

Mathematics I — Week 12: Full Course Synthesis & Exam Revision Map
The IITM syllabus lists Week 12 as revision, so there is no new topic to introduce here.
This is the useful version of Week 12: a full-course compression layer for everything from sets to graph algorithms.
0. Entire course in one map
textW1 Sets → Relations → Functions ↓ W2 Coordinates → Straight Lines ↓ W3 Quadratics ↓ W4 General Polynomials ↓ W5 Functions → Composition → Inverses → Exponentials ↓ W6 Logarithms ↓ W7 Sequences → Limits ↓ W8 Derivatives ↓ W9 Integrals ↓ W10 Graph Theory → BFS/DFS → DAGs ↓ W11 Weighted Graphs → Shortest/Longest Paths
There are really three large mathematical blocks:
- Algebra + functions: Weeks 1–6
- Calculus: Weeks 7–9
- Discrete maths + graph algorithms: Weeks 10–11
Part I — Weeks 1–6
Algebra, Functions & Their Graphs
Week 1 — Sets, Relations & Functions
Core set notation
Membership:
Subset:
Empty set:
Cardinality:
Power set:
If:
then:
Set operations
Union:
Intersection:
Difference:
Complement:
De Morgan:
Cartesian product
A×B={(a,b):a∈A,b∈B}If:
then:
Relation properties — must know cold
Reflexive
aRafor every a.
Symmetric
aRb⇒bRaAntisymmetric
aRb∧bRa⇒a=bTransitive
aRb∧bRc⇒aRcEquivalence relation
reflexive + symmetric + transitivePartial order
reflexive + antisymmetric + transitiveHighest-value trap
Antisymmetric does not mean "not symmetric."
A relation can be both.
Functions
For:
- A = domain
- B = codomain
- actual outputs = range
Always:
Injective / one-one
f(a)=f(b)⇒a=bSurjective / onto
Range=CodomainBijective
injective + surjectiveBijective functions admit full inverses.
Week 2 — Coordinate Geometry & Straight Lines
Distance
d=(x2−x1)2+(y2−y1)2Midpoint
(2x1+x2,2y1+y2)Slope
m=x2−x1y2−y1Types of slopes
Positive:
Negative:
Horizontal:
Vertical:
Line equations
Slope-intercept
y=mx+cPoint-slope
y−y1=m(x−x1)General form
Ax+By+C=0Slope from general form:
Parallel lines
m1=m2Perpendicular lines
m1m2=−1except horizontal/vertical edge case.
Collinearity
Three points are collinear if their slopes agree appropriately, or equivalently:
Week 3 — Quadratics
General form:
with:
Direction
If:
opens upward → minimum.
If:
opens downward → maximum.
Axis of symmetry
x=−2abVertex
(−2ab,f(−2ab))Quadratic formula
x=2a−b±b2−4acDiscriminant:
Nature of roots
If:
→ two distinct real roots.
If:
→ repeated real root.
If:
→ no real roots.
Root relationships
If roots are:
then:
Three useful quadratic forms
Standard:
Factored:
Vertex:
Each exposes different information.
Week 4 — Polynomials
General polynomial:
Degree:
Leading coefficient:
Division algorithm
P(x)=D(x)Q(x)+R(x)with:
Remainder theorem
When dividing by:
remainder is:
Factor theorem
x−a factor⟺P(a)=0Thus:
Multiplicity
For:
r is a root of multiplicity m.
Odd multiplicity:
Even multiplicity:
End behavior
Determined by:
- parity of degree
- sign of leading coefficient
| Degree | Leading coefficient | Left | Right |
|---|---|---|---|
| even | + | up | up |
| even | - | down | down |
| odd | + | down | up |
| odd | - | up | down |
Shortcut:
even degree → same-direction ends
odd degree → opposite-direction ends
Week 5 — Composition, Inverses & Exponentials
Vertical line test
Tests whether graph is a function.
Horizontal line test
Tests whether function is one-one.
Composition
(f∘g)(x)=f(g(x))Order matters:
in general.
Inverse
If:
then:
Core identities:
Inverse trap
f−1(x)=f(x)1Finding inverse
- Write: y=f(x)
- Swap x,y.
- Solve for y.
- Check domain/range.
Exponential function
f(x)=axwhere:
Domain:
Range:
If:
→ growth.
If:
→ decay.
Week 6 — Logarithms
Fundamental equivalence:
Restrictions:
Log laws
Product:
Quotient:
Power:
Change of base:
Inverse identities
loga(ax)=x alogax=xCommon log trap
Wrong:
There is no such law.
Part II — Weeks 7–9
Calculus
Week 7 — Sequences & Limits
Sequence
A sequence is essentially:
Sequence limit
n→∞liman=Lmeans terms approach L as n becomes large.
Basic limit
n→∞limnp1=0for:
Rational degree rule
For:
if numerator degree < denominator degree:
If equal:
If numerator degree > denominator degree:
usually unbounded or requires sign analysis.
Function limits
x→alimf(x)=Lmeans nearby values of f(x) approach L.
The exact value:
may differ.
Left and right limits
x→a−limf(x)and:
Two-sided limit exists iff:
Continuity
At x=a:
with both sides existing.
0/0 trap
00is not the answer.
It is an indeterminate form.
Typical remedies:
- factor
- cancel
- rationalize
Tangent preview
Secant slope:
Tangent slope:
Week 8 — Derivatives
Derivative definition:
Interpretations:
- tangent slope
- instantaneous rate of change
- local behavior
Differentiability relationship
differentiable⇒continuousBut:
Example:
at 0.
Derivative rules
Constant:
Power:
Product:
Quotient:
Chain:
Common derivatives
dxdex=ex dxdax=axlna dxdlnx=x1Tangent line
At:
Increasing/decreasing
If:
→ increasing.
If:
→ decreasing.
Critical points
Candidates occur when:
or derivative is undefined while function exists.
First derivative test
+→−→ local maximum.
→ local minimum.
L’Hôpital
For suitable:
or:
when conditions are satisfied.
Do not confuse this with the quotient rule.
Week 9 — Integration
Indefinite integral:
where:
Power rule
For:
Special case:
Exponential integral
∫exdx=ex+CFundamental Theorem of Calculus
If:
then:
Signed area
Above x-axis:
Below x-axis:
Therefore definite integral is net signed area.
For total geometric area, split intervals or use:
Area between curves
If:
then:
Think:
top minus bottom.
Substitution
Reverse chain rule:
Calculus in one picture
textLIMIT "What happens nearby?" ↓ DERIVATIVE "How fast is it changing?" ↓ INTEGRAL "How much accumulated?"
And:
through the Fundamental Theorem.
Part III — Weeks 10–11
Graph Theory & Algorithms
Week 10 — Graph Fundamentals
Graph:
where:
- V = vertices
- E = edges
Degree
Undirected:
Handshaking lemma:
Directed graphs
In-degree:
Out-degree:
and:
Complete graph
For:
Representations
Adjacency matrix:
if edge exists.
Adjacency list:
textA: B, C B: A C: A
Adjacency list space:
Adjacency matrix space:
BFS
Breadth-First Search:
- queue
- level-by-level exploration
- shortest paths in unweighted graphs
Complexity:
DFS
Depth-First Search:
- stack/recursion
- explores deeply
- backtracking
Complexity:
Tree
A tree is:
- connected
- acyclic
and for n vertices:
DAG
DAG:
No directed cycles.
Useful for:
- dependencies
- scheduling
- prerequisites
A topological ordering exists iff graph is a DAG.
Week 11 — Weighted Graph Algorithms
Path weight
∑w(e)along the path.
Relaxation
Core shortest-path operation:
Algorithm choice
Unweighted
BFSWeighted, nonnegative
DijkstraNegative weights
Bellman–FordDijkstra
Condition:
Greedy strategy:
repeatedly finalize nearest unprocessed vertex.
Typical efficient complexity:
Bellman–Ford
Handles negative edges.
Relax all edges:
times.
Complexity:
One additional successful relaxation indicates a reachable negative cycle.
Negative cycle
If total cycle weight is negative, you can loop repeatedly and reduce cost indefinitely.
Therefore finite shortest path may not exist.
Longest path in DAG
On DAGs:
Process vertices in topological order.
Transitive closure
Answers:
Is v reachable from u?
It adds:
whenever a path already exists.
Adjacency matrix powers
If A is adjacency matrix:
counts walks of length:
from i to j.
Important:
walks, not necessarily simple paths.
1. Highest-value formula sheet
If I had to compress Maths 1 to one page, these are the formulas I'd keep.
Sets
∣P(A)∣=2∣A∣ ∣A×B∣=∣A∣∣B∣Line
m=x2−x1y2−y1 y−y1=m(x−x1) m1m2=−1Quadratic
xv=−2ab x=2a−b±b2−4ac Δ=b2−4acPolynomial
P(a)=remainder on division by x−a P(a)=0⟺x−a factorComposition/inverse
(f∘g)(x)=f(g(x)) f−1(f(x))=xLogs
ax=y⟺logay=x log(xy)=logx+logy log(xr)=rlogxLimits
x→a−limf(x)=x→a+limf(x)for two-sided existence.
Derivative
f′(x)=h→0limhf(x+h)−f(x) (xn)′=nxn−1 (fg)′=f′g+fg′ (gf)′=g2f′g−fg′Integral
∫xndx=n+1xn+1+C ∫abf=F(b)−F(a)Graphs
v∑deg(v)=2∣E∣ BFS/DFS=O(V+E) tree: ∣E∣=∣V∣−12. Most dangerous conceptual traps
These are more important than memorizing fifty extra formulas.
Trap 1
Symmetric:
is not the same as antisymmetric:
Trap 2
Vertical line test:
Is it a function?
Horizontal line test:
Is it one-one?
Trap 3
f−1(x)does not mean:
Trap 4
log(x+y)=logx+logy.Trap 5
00is not zero.
It means indeterminate.
Trap 6
Continuity does not guarantee differentiability.
Trap 7
f′(x)=0does not automatically mean minimum or maximum.
Trap 8
Definite integral is signed area.
Trap 9
DFS does not generally give shortest paths.
Trap 10
Dijkstra cannot safely handle negative edge weights.
3. Dependency map
Some topics depend heavily on earlier ones.
textFunctions ├── composition │ └── chain rule │ ├── inverse │ └── logarithms │ └── graphs ├── limits ├── derivatives └── integrals Relations └── graph theory ├── adjacency ├── reachability └── transitive closure Matrices └── adjacency matrix powers
The syllabus isn't really eleven isolated chapters.
It is a connected dependency graph.
4. Suggested exam-priority hierarchy
If prep time becomes constrained, I would prioritize roughly:
Tier A — must be reliable
- relation properties
- functions/injectivity/surjectivity
- straight-line equations and slope
- quadratic roots/vertex/discriminant
- polynomial roots/factor/remainder theorem
- composition/inverse
- logarithm rules
- limits
- derivative rules
- critical points
- basic integration
- BFS vs DFS
- Dijkstra vs Bellman–Ford
Tier B — important
- multiplicity/end behavior
- continuity
- tangent equations
- L’Hôpital
- area between curves
- DAG/topological ordering
- transitive closure
- adjacency matrix powers
Tier C — polish after fundamentals
- deeper proofs
- rarer graph identities
- unusual transformations
- more elaborate limit manipulations
5. The course as mental models
If formulas disappear from memory, these intuitions can reconstruct a lot.
Sets
Which objects belong?
Relation
Which pairs are connected?
Function
Each input gets exactly one output.
Slope
change in y per change in x.
Quadratic
symmetric curved function with one turning point.
Polynomial
degree + roots + multiplicities + leading term determine much of the graph.
Exponential
repeated multiplicative growth.
Logarithm
what exponent produced this value?
Limit
what value is behavior approaching?
Derivative
instantaneous local change.
Integral
accumulated change.
Graph
objects connected by relationships.
BFS
explore outward in layers.
DFS
go deep, then backtrack.
Dijkstra
repeatedly lock in nearest safe vertex.
Bellman–Ford
repeatedly propagate improvements everywhere.
6. Final mastery checklist
You should be able to answer yes to these:
- Can I distinguish all four relation properties?
- Can I classify injective/surjective/bijective functions?
- Can I construct a line from two points?
- Can I analyze a quadratic completely?
- Can I infer polynomial graph behavior from factors?
- Can I use remainder/factor theorem quickly?
- Can I compose functions without reversing order?
- Can I find an inverse and respect its domain?
- Can I solve exponential/log equations?
- Can I evaluate basic limits and recognize 0/0?
- Can I differentiate standard functions correctly?
- Can I identify extrema from f′?
- Can I integrate basic polynomials and compute definite integrals?
- Can I distinguish signed area from actual area?
- Can I trace BFS and DFS?
- Can I recognize trees and DAGs?
- Can I choose BFS vs Dijkstra vs Bellman–Ford?
- Can I interpret adjacency-matrix powers?
If most of those are solid, you have the core conceptual coverage of Maths 1.
Quick Recall & Flashcards
Test your active recall before proceeding to practice problems. Format:
Question :: Answer.- Equivalence Relation vs Partial Order distinction :: Equivalence is Symmetric; Partial Order is Antisymmetric
- Vertex of parabola vs Critical point of derivative :: Vertex x=−b/(2a) is where f′(x)=2ax+b=0
- Shortest path algorithm decision: Unweighted graph :: Breadth-First Search (BFS) in O(∣V∣+∣E∣)
- Shortest path algorithm decision: Nonnegative weights :: Dijkstra's Algorithm in O((∣V∣+∣E∣)log∣V∣)
- Shortest path algorithm decision: General weights (negative edges allowed) :: Bellman–Ford Algorithm in O(∣V∣⋅∣E∣)
- Fundamental theorem linking derivatives and integrals :: Fundamental Theorem of Calculus (FTC): accumulation vs rate of change
Practice & Self-Check
Attempt these diagnostic questions to verify your operational understanding.
1
Question 1
If a directed graph G has a topological ordering, which of the following MUST be true?
MCQ
3 Unit Assessment
A. G has no directed cycles (it is a DAG)
B. G is an undirected tree
C. G is strongly connected
D. Every vertex has indegree equal to outdegree
2
Question 2
A critical point of a differentiable function f(x) occurs where:
MCQ
3 Unit Assessment
A. f(x)=0
B. f′(x)=0 or f′(x) is undefined
C. f′′(x)>0
D. f(x)=f(−x)