Machine Learning (CS31002) Overview

Podcast

Play an AI-generated podcast conversation about this lesson

Questions and Answers

What is one of the main objectives of the CS31002 Machine Learning course?

To understand the principles and concepts of machine learning.

Name one type of model students will learn about in the CS31002 Machine Learning course.

Regression model, classification model or clustering model.

Besides traditional algorithms, what specific type of neural network will be covered in the CS31002 Machine Learning course?

Convolutional neural networks (CNN).

What is one skill students will develop to help build real-world machine learning applications?

Programming skills. Signup and view all the answers

What is one of the course outcomes for students completing CS31002 Machine Learning?

Implement algorithms using machine learning tools OR design and implement various machine learning algorithms in a range of real-world applications. Signup and view all the answers

What is the primary purpose of normalization and standardization in the context of machine learning?

Normalization and standardization are primarily used to scale and center features, ensuring that all input variables have a similar range of values, which helps in faster convergence of algorithms and prevents features with large values from dominating others. Signup and view all the answers

Explain the concept of the 'bias-variance tradeoff' in machine learning.

The bias-variance tradeoff refers to the balance between a model's ability to fit training data (low bias) and its ability to generalize to unseen data (low variance). A model with high bias is typically underfit and a model with high variance is overfit. Signup and view all the answers

How do L1 (Lasso) and L2 (Ridge) regularization differ in their approach to reducing model complexity?

L1 regularization (Lasso) adds the absolute values of the weights to the cost function, which can drive some weights to exactly zero, resulting in feature selection. L2 regularization (Ridge) adds the squared values, which shrinks the weights towards zero, but rarely to exactly zero. Signup and view all the answers

In classification, briefly describe the meaning of 'precision' and 'recall'.

Precision is the proportion of true positives (correct positive predictions) out of all instances that the model predicted as positive. Recall is the proportion of true positives out of all actual positives in the data. Signup and view all the answers

What is the primary goal of Principal Component Analysis (PCA)?

The primary goal of PCA is to reduce dimensionality of high-dimensional data, by transforming the features into a new set of uncorrelated variables called principal components, retaining as much of the variance as possible using a smaller number of features. Signup and view all the answers

How does the DBSCAN clustering algorithm differ from K-means clustering?

DBSCAN is a density-based clustering algorithm that does not require the number of clusters to be specified and is adept at finding clusters of arbitrary shape. K-Means is a partition-based algorithm, that is much more sensitive to the initial selection of the means and tends to work well with spherical data. Signup and view all the answers

Explain the concept of an activation function in the context of neural networks.

An activation function introduces non-linearity into a neural network enabling it to learn complex patterns. It determines whether a neuron should fire (activate) based on the weighted sum of its inputs. Signup and view all the answers

What are vanishing and exploding gradient problems in neural networks, and why do they occur?

Vanishing gradients and exploding gradients are issues where gradients during backpropagation become extremely small or large respectively. Vanishing gradients are caused by some activation functions that saturate at the extreme ends, and exploding gradients are caused by overly large coefficients in the network. Signup and view all the answers

What is the purpose of data augmentation in the training of convolutional neural networks (CNNs)?

Data augmentation artificially increases the size of the training dataset by creating modified versions of the existing images. This helps reduce overfitting, improves network generalization and allows a model to learn invariance to small changes. Signup and view all the answers

What are some typical tools or libraries used in machine learning?

Common machine learning tools or libraries include Scikit-learn, TensorFlow, PyTorch, and Kaggle. These provide various functionalities for building, training, and evaluating machine learning models and also allow for dataset management. Signup and view all the answers

Study Notes

Course Information

Course: Machine Learning (CS31002)
Credits: 4
Session: December 2024 to May 2025
Coordinator: Dr. Partha Pratim Sarangi ([email protected])
Co-coordinator: Dr. Santos Kumar Baliarsingh ([email protected])

Course Objective

Provide a broad survey of different machine learning approaches and techniques
Understand the principles and concepts of machine learning
Learn regression and classification models
Learn different clustering models
Understand artificial neural networks (ANN) and convolutional neural networks (CNN) concepts
Develop programming skills to build real-world applications based on machine learning

Course Outcomes

Solve typical machine learning problems
Compare and contrast different data representations to facilitate learning
Apply regression, classification, and clustering methods
Suggest supervised/unsupervised machine learning approaches for any application
Implement algorithms using machine learning tools
Design and implement various machine learning algorithms in real-world applications

Module 1

Lecture 1: Introduction to Machine Learning (definition and real-world applications)
Lecture 2: Types of Machine Learning (supervised, unsupervised, semi-supervised)
Lecture 3: Regression (Linear Regression, intuition, cost function)
Lecture 4: Linear Regression (Gradient Descent)
Lecture 5: Multiple Linear Regression
Lecture 6: Closed-form Equation, types of Gradient Descent (Batch, Stochastic, Mini-batch), properties
Lecture 7: Normalization, Standardization, overfitting, and underfitting
Lecture 8: Bias, Variance, Bias-Variance tradeoff
Lecture 9: Regularization (Lasso, Ridge)
Lecture 10: Activity 1 (likely an assignment or exercise)

Module 2

Lecture 11: Classification (Logistic Regression - 1 binary)
Lecture 12: Logistic Regression (2 binary)
Lecture 13: Nearest Neighbor and K-Nearest Neighbor
Lecture 14: Error Analysis (Train/Test split, validation set, accuracy, precision, recall, F-measure, ROC curve, confusion matrix)
Lecture 15: Naive Bayes Classifier 1
Lecture 16: Naive Bayes Classifier 2
Lecture 17: Decision Tree (introduction, ID3 algorithm)
Lecture 18: Decision Tree (ID3 Algorithm)
Lecture 19: Decision Tree (problem of overfitting, pre-pruning/post-pruning, examples)
Lecture 20: Support Vector Machine (Terminologies, intuition, learning, derivation)
Lecture 21: Support Vector Machine (terminologies, intuition, learning, derivation)
Lecture 22: Support Vector Machine (KKT condition)
Lecture 23: Support Vector Machine (kernel, non-linear classification)
Lecture 24: Activity 2 (likely an assignment or exercise)

Module 3

Lecture 25: Principal Component Analysis (Steps, merits, demerits, intuition)
Lecture 26: Principal Component Analysis (Steps, merits, demerits, intuition)
Lecture 27: Understanding PCA using SVD for dimensionality reduction
Lecture 28: Clustering (introduction, K-means Clustering)
Lecture 29: K-means Clustering (numerical example)
Lecture 30: K-means Clustering (further details)
Lecture 31: DBScan Clustering (why used, parameters, characterization, steps)
Lecture 32: Mean Shift Clustering (time/space complexities)
Lecture 33: Hierarchical Clustering (agglomerative, Single/Complete/Average/Centroid Linkage)
Lecture 34: Hierarchical Clustering (divisive hierarchical)
Lecture 35: Activity 3 (likely an assignment or exercise)

Module 4

Lecture 36: Introduction to Neural Networks (McCulloch-Pitts Neuron)
Lecture 37: Least Mean Square (LMS) Algorithm
Lecture 38: Perceptron Model
Lecture 39: Multilayer Perceptron (MLP) and Hidden Layer Representation
Lecture 40: Non-linear Problem Solving, Activation Functions
Lecture 41: Backpropagation Algorithm 1
Lecture 42: Backpropagation Algorithm 2
Lecture 43: Exploding/Vanishing Gradient Problem
Lecture 44: Introduction to Convolutional Neural Networks (CNN)
Lecture 45: CNN Structure
Lecture 46: Data Augmentation, Batch Normalization, Dropout

Module 5

Lecture 47: Activity 4 (likely an assignment or exercise)
Lecture 48: Introduction to Machine Learning Tools (Scikit-Learn, PyTorch, TensorFlow, Kaggle competitions), Case Studies (Classification using Iris Dataset, Feature Extraction, Linear Regression, Clustering, Classification of MNIST Dataset using CNN)

Assessments

Quizzes (before and after mid-semester)
Assignments/coding assignments (before and after mid-semester)

Textbooks and References

Various textbooks are listed, including those on machine learning, probabilistic machine learning, neural networks, and more.

Studying That Suits You

Use AI to generate personalized quizzes and flashcards to suit your learning preferences.

Description

Explore the fundamentals of Machine Learning through CS31002. This quiz covers key concepts such as regression, classification, clustering models, and artificial neural networks. Prepare to apply various machine learning approaches in real-world applications.