BSMA3014 · workspace

Statistical Computing

Syllabus, study tools, lectures, and curriculum map.

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Weekly outline

Syllabus

Week topics from the course map

00W00

Topic

Incomplete

01W01

Introduction to R, Introduction to Monte Carlo, Pseudorandom Number Generation, Sampling Discrete Random Variables: Inverse Transform Method

Incomplete

02W02

Discrete: Accept-Reject Algorithm, Composition Method, Sampling Continuous Random Variables: Inverse Transform Method

Incomplete

03W03

Continuous: Accept-reject Algorithm with examples, Box-Muller method

Incomplete

04W04

Continuous: Ratio-of-Uniforms method, examples and code, miscellaneous methods in sampling, Sampling from multivariate distritbutions

Incomplete

05W05

Simple Importance Sampling: Examples, bias, variance, consistency, Optimal proposals,

Incomplete

06W06

Weighted importance sampling: Examples, Review of likelihood functions, MLE examples

Incomplete

07W07

Linear regression as MLE, Penalized regression, No-closed form MLEs, Review of Taylor Series Approximations

Incomplete

08W08

Newton's optimization algorithm: examples and code, Gradient Descent algorithm, applications to logistic regression with code

Incomplete

09W09

MM algorithm, application to Bridge Regression, EM algorithm, Introduction to Gaussian Mixture Model

Incomplete

010W10

EM algorithm for GMM, Cross-validation with examples

Incomplete

011W11

Bootstrapping: examples and code. Application to bridge regression, stochastic gradient descent

Incomplete

012W12

Applications of SGD with code. Simulated annealing: examples, codes, and challenges

Incomplete

Study tools

Memorize first

Compact recall strip

Open notes

1 min

Advanced Optimization

Open this first for the core logic and traps.

Dive in

1 min

Bootstrap Methods

Open this first for the core logic and traps.

Dive in

1 min

Cross-Validation

Open this first for the core logic and traps.

Dive in

Practice Dock

One drill at a time, with Theo hints, teacher lanes, and a replay queue.

Use this when the theory is already clear and the goal is fast, clean retrieval under pressure. Mark weak questions, copy a revision pack, and move on.

TheoTeacherTextbookSpeedrun

Ask less. Recall more.

Open Practice Dock

SM-2 Active

Spaced Repetition

Flashcards

Spaced repetition engine for long-term retention of core concepts.

Start session

Not Available

Interactive Simulations

Visual Labs

Exam Mode

Adaptive Practice

Mock Exam

Set paper

Code Protocol

OPPE Simulator

Secure Comm-Link Terminal

Secure Comm-Link // Playlist Connected

Uplink 12ms

Syllabus Synchronization: Active

Open current cue

Syllabus Matrix Registry

Global Course Index

Open Full Frame

Master Registry

v6.4 Directory

Foundational

Diploma

BSc Degree

BS Degree

PG / MTech

BSMA3014

BSc Degree

4 Credits

Statistical Computing

To introduce computational methods involved in statistical estimation and learning problems.

Deployment Hub External Docs

Execution Protocol

Module 0

Topic

Module 1

Introduction to R, Introduction to Monte Carlo, Pseudorandom Number Generation, Sampling Discrete Random Variables: Inverse Transform Method

Module 2

Discrete: Accept-Reject Algorithm, Composition Method, Sampling Continuous Random Variables: Inverse Transform Method

Module 3

Continuous: Accept-reject Algorithm with examples, Box-Muller method

Module 4

Continuous: Ratio-of-Uniforms method, examples and code, miscellaneous methods in sampling, Sampling from multivariate distritbutions

Module 5

Simple Importance Sampling: Examples, bias, variance, consistency, Optimal proposals,

Module 6

Weighted importance sampling: Examples, Review of likelihood functions, MLE examples

Module 7

Linear regression as MLE, Penalized regression, No-closed form MLEs, Review of Taylor Series Approximations

Module 8

Newton's optimization algorithm: examples and code, Gradient Descent algorithm, applications to logistic regression with code

Module 9

MM algorithm, application to Bridge Regression, EM algorithm, Introduction to Gaussian Mixture Model

Module 10

EM algorithm for GMM, Cross-validation with examples

Module 11

Bootstrapping: examples and code. Application to bridge regression, stochastic gradient descent

Module 12

Syllabus

Topic

Introduction to R, Introduction to Monte Carlo, Pseudorandom Number Generation, Sampling Discrete Random Variables: Inverse Transform Method

Discrete: Accept-Reject Algorithm, Composition Method, Sampling Continuous Random Variables: Inverse Transform Method

Continuous: Accept-reject Algorithm with examples, Box-Muller method

Continuous: Ratio-of-Uniforms method, examples and code, miscellaneous methods in sampling, Sampling from multivariate distritbutions

Simple Importance Sampling: Examples, bias, variance, consistency, Optimal proposals,

Weighted importance sampling: Examples, Review of likelihood functions, MLE examples

Linear regression as MLE, Penalized regression, No-closed form MLEs, Review of Taylor Series Approximations

Newton's optimization algorithm: examples and code, Gradient Descent algorithm, applications to logistic regression with code

MM algorithm, application to Bridge Regression, EM algorithm, Introduction to Gaussian Mixture Model

EM algorithm for GMM, Cross-validation with examples

Bootstrapping: examples and code. Application to bridge regression, stochastic gradient descent

Applications of SGD with code. Simulated annealing: examples, codes, and challenges

Compact recall strip

One drill at a time, with Theo hints, teacher lanes, and a replay queue.

Flashcards

Visual Labs

Mock Exam

OPPE Simulator

Secure Comm-Link Terminal

Syllabus Matrix Registry

Master Registry

Foundational

Diploma

BSc Degree

BS Degree

PG / MTech

Statistical Computing

Execution Protocol

Topic

Introduction to R, Introduction to Monte Carlo, Pseudorandom Number Generation, Sampling Discrete Random Variables: Inverse Transform Method

Discrete: Accept-Reject Algorithm, Composition Method, Sampling Continuous Random Variables: Inverse Transform Method

Continuous: Accept-reject Algorithm with examples, Box-Muller method

Continuous: Ratio-of-Uniforms method, examples and code, miscellaneous methods in sampling, Sampling from multivariate distritbutions

Simple Importance Sampling: Examples, bias, variance, consistency, Optimal proposals,

Weighted importance sampling: Examples, Review of likelihood functions, MLE examples

Linear regression as MLE, Penalized regression, No-closed form MLEs, Review of Taylor Series Approximations

Newton's optimization algorithm: examples and code, Gradient Descent algorithm, applications to logistic regression with code

MM algorithm, application to Bridge Regression, EM algorithm, Introduction to Gaussian Mixture Model

EM algorithm for GMM, Cross-validation with examples

Bootstrapping: examples and code. Application to bridge regression, stochastic gradient descent

Applications of SGD with code. Simulated annealing: examples, codes, and challenges

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