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Ml Techniques

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Syllabus

Week topics from the course map

00W00

Topic

Incomplete

01W01

Introduction; Unsupervised Learning - Representation learning - PCA

Incomplete

02W02

Unsupervised Learning - Representation learning - Kernel PCA

Incomplete

03W03

Unsupervised Learning - Clustering - K-means/Kernel K-means

Incomplete

04W04

Unsupervised Learning - Estimation - Recap of MLE + Bayesian estimation, Gaussian Mixture Model - EM algorithm.

Incomplete

05W05

Supervised Learning - Regression - Least Squares; Bayesian view

Incomplete

06W06

Supervised Learning - Regression - Ridge/LASSO

Incomplete

07W07

Supervised Learning - Classification - K-NN, Decision tree

Incomplete

08W08

Supervised Learning - Classification - Generative Models - Naive Bayes

Incomplete

09W09

Discriminative Models - Perceptron; Logistic Regression

Incomplete

010W10

Support Vector Machines

Incomplete

011W11

Ensemble methods - Bagging and Boosting (Adaboost)

Incomplete

012W12

Artificial Neural networks: Multiclass classification.

Incomplete

Study tools

Memorize first

Compact recall strip

Open notes

1 min

ML Techniques: Course Overview

Open this first for the core logic and traps.

Dive in

2 min

Anomaly Detection

Open this first for the core logic and traps.

Dive in

2 min

Deep Learning Foundations

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.

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Foundational

Diploma

BSc Degree

BS Degree

PG / MTech

BSCS2007

Diploma

4 Credits

Machine Learning Techniques

To introduce the main methods and models used in machine learning problems of regression, classification and clustering. To study the properties of...

Deployment Hub External Docs

Execution Protocol

Module 0

Topic

Module 1

Introduction; Unsupervised Learning - Representation learning - PCA

Module 2

Unsupervised Learning - Representation learning - Kernel PCA

Module 3

Unsupervised Learning - Clustering - K-means/Kernel K-means

Module 4

Unsupervised Learning - Estimation - Recap of MLE + Bayesian estimation, Gaussian Mixture Model - EM algorithm.

Module 5

Supervised Learning - Regression - Least Squares; Bayesian view

Module 6

Supervised Learning - Regression - Ridge/LASSO

Module 7

Supervised Learning - Classification - K-NN, Decision tree

Module 8

Supervised Learning - Classification - Generative Models - Naive Bayes

Module 9

Discriminative Models - Perceptron; Logistic Regression

Module 10

Support Vector Machines

Module 11

Ensemble methods - Bagging and Boosting (Adaboost)

Module 12

Syllabus

Topic

Introduction; Unsupervised Learning - Representation learning - PCA

Unsupervised Learning - Representation learning - Kernel PCA

Unsupervised Learning - Clustering - K-means/Kernel K-means

Unsupervised Learning - Estimation - Recap of MLE + Bayesian estimation, Gaussian Mixture Model - EM algorithm.

Supervised Learning - Regression - Least Squares; Bayesian view

Supervised Learning - Regression - Ridge/LASSO

Supervised Learning - Classification - K-NN, Decision tree

Supervised Learning - Classification - Generative Models - Naive Bayes

Discriminative Models - Perceptron; Logistic Regression

Support Vector Machines

Ensemble methods - Bagging and Boosting (Adaboost)

Artificial Neural networks: Multiclass classification.

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 Techniques

Execution Protocol

Topic

Introduction; Unsupervised Learning - Representation learning - PCA

Unsupervised Learning - Representation learning - Kernel PCA

Unsupervised Learning - Clustering - K-means/Kernel K-means

Unsupervised Learning - Estimation - Recap of MLE + Bayesian estimation, Gaussian Mixture Model - EM algorithm.

Supervised Learning - Regression - Least Squares; Bayesian view

Supervised Learning - Regression - Ridge/LASSO

Supervised Learning - Classification - K-NN, Decision tree

Supervised Learning - Classification - Generative Models - Naive Bayes

Discriminative Models - Perceptron; Logistic Regression

Support Vector Machines

Ensemble methods - Bagging and Boosting (Adaboost)

Artificial Neural networks: Multiclass classification.

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