kanban-plugin
board

Week 1

11th Dec :
- Counting
- [[Probability Axioms]]
- Sample Space
- Events
12th December :
- Independent and Mutually exclusive events
- Marginal Probability
- Conditional Probability
- Joint Probability
- Bayes Theorem
13th December :
- Conditional Expectation and Variance
- Mean, Median, Mode
- Standard Deviation
14th December :
- Correlation
- Covariance
- Random Variables
- Discrete Random Variables
- PMFs
15th December :
- Uniform Distribution
- Bernoulli Distribution
- Binomial Distribution
16th December:
- Continuous Random Variables :
- Uniform
- Exponential
- Poisson Distribution
17th December:
- Normal Distribution
- Standard Normal Distribution
- T-Distribution
- $\chi^{2}$ distribution
- CDF

Week 2

18th December :
- Vector Space
- Subspaces
- Linear Dependence and Independence
19th December :
- Matrices
- Projection Matrix
- Orthogonal Matrix
- Idempotent Matrix
20th December :
- Quadratic Forms
- Systems of Linear Equations
- Gaussian Elimination
21st December :
- Eigenvalues and Eigenvectors
- Determinants
- Rank
- Nullity
22nd December :
- LU Decomposition
- Singular Value Decomposition
23rd December :
- Practice Linear Algebra
24th December :
- Practice Probability

25th December :
- Functions of a Single Variable
- Limits
- Continuity and Differentiability
26th December :
- Taylor Series
- Maxima and Minima
- Single Variabel Optimization
27th December :
- Stacks and Queues implementation in Python
28th December :
- Linked Lists
- Trees
- Hash Tables
29th December :
- Linear Search
- Binary Search
- Selection Sort
- Bubble Sort
- Insertion Sort
30th December :
- Divide and Conquer Algorithms
- MergeSort
- QuickSort
31st December :
- Graph Theory Basics
- Traversals
- Shortest Path

1st Jan 2025:
- ER-Model
- Relational Algebra
- Tuple Calculus
- SQL
2nd January :
- Integrity Constraints
- Normalization
- Discretization
- Sampling Compression
3rd January :
- Multidimensional Data Models
- COncept Hierarchies
- Measures
4th January :
- Supervised Learning : Regression Problems
- Simple/Multiple Linear Regression
- Ridge Regression
5th January :
- Logistic Regression
- k_NN
- Naive Bayes
- Linear Discriminant Analysis
- SVM
6th January :
- Clustering Algorithms :
- k-means/k-medoid
- Hierarchical Clustering
- Principal Component Analysis
7th January :
- AI: Informed Search
- Uninformed Search
- Adversarial Search
- Logical ( Propositional)
- Logic(Predicate)

%% kanban:settings

{"kanban-plugin":"board","list-collapse":[false,false,false,false]}

%%