BSCS2004 · workspace

Ml Foundations

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Syllabus

Week topics from the course map

00W00

Topic

Incomplete

01W01

Introduction to machine learning

Incomplete

02W02

Calculus

Incomplete

03W03

Linear Algebra - Least Squares Regression

Incomplete

04W04

Linear Algebra - Eigenvalues and eigenvectors

Incomplete

05W05

Linear Algebra - Symmetric matrices

Incomplete

06W06

Linear Algebra - Singular value decomposition, Principal Component Analysis in Image Processing

Incomplete

07W07

Unconstrained Optimisation

Incomplete

08W08

Convex sets, functions, and optimisation problems

Incomplete

09W09

Constrained Optimisation and Lagrange Multipliers. Logistic regression as an optimization problem

Incomplete

010W10

Examples of probabilistic models in machine learning problems

Incomplete

011W11

Exponential Family of distributions

Incomplete

012W12

Parameter estimation. Expectation Maximization.

Incomplete

Study tools

Memorize first

Compact recall strip

Open notes

3 min

Boosting: AdaBoost, Gradient Boosting & XGBoost

Open this first for the core logic and traps.

Dive in

3 min

Gradient Descent Variants

Open this first for the core logic and traps.

Dive in

9 min

Clustering: k-Means, Hierarchical, DBSCAN

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

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Interactive Simulations

Visual Labs

Exam Mode

Adaptive Practice

Mock Exam

Set paper

Code Protocol

OPPE Simulator

Secure Comm-Link Terminal

Secure Comm-Link // @mlf-iitm

Uplink 12ms

Syllabus Synchronization: Active

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Master Registry

v6.4 Directory

Foundational

Diploma

BSc Degree

BS Degree

PG / MTech

BSCS2004

Diploma

4 Credits

Machine Learning Foundations

This course lays the groundwork for the upcoming ML courses by covering various fundamentals that do not necessarily fall under Machine Learning bu...

Deployment Hub External Docs

Execution Protocol

Module 0

Topic

Module 1

Introduction to machine learning

Module 2

Calculus

Module 3

Linear Algebra - Least Squares Regression

Module 4

Linear Algebra - Eigenvalues and eigenvectors

Module 5

Linear Algebra - Symmetric matrices

Module 6

Linear Algebra - Singular value decomposition, Principal Component Analysis in Image Processing

Module 7

Unconstrained Optimisation

Module 8

Convex sets, functions, and optimisation problems

Module 9

Constrained Optimisation and Lagrange Multipliers. Logistic regression as an optimization problem

Module 10

Examples of probabilistic models in machine learning problems

Module 11

Exponential Family of distributions

Module 12

Syllabus

Topic

Introduction to machine learning

Calculus

Linear Algebra - Least Squares Regression

Linear Algebra - Eigenvalues and eigenvectors

Linear Algebra - Symmetric matrices

Linear Algebra - Singular value decomposition, Principal Component Analysis in Image Processing

Unconstrained Optimisation

Convex sets, functions, and optimisation problems

Constrained Optimisation and Lagrange Multipliers. Logistic regression as an optimization problem

Examples of probabilistic models in machine learning problems

Exponential Family of distributions

Parameter estimation. Expectation Maximization.

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

Machine Learning Foundations

Execution Protocol

Topic

Introduction to machine learning

Calculus

Linear Algebra - Least Squares Regression

Linear Algebra - Eigenvalues and eigenvectors

Linear Algebra - Symmetric matrices

Linear Algebra - Singular value decomposition, Principal Component Analysis in Image Processing

Unconstrained Optimisation

Convex sets, functions, and optimisation problems

Constrained Optimisation and Lagrange Multipliers. Logistic regression as an optimization problem

Examples of probabilistic models in machine learning problems

Exponential Family of distributions

Parameter estimation. Expectation Maximization.

Video Archive

Intelligence Hub