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Introduction to Machine Learning and
Deep Learning
Towards a light-weight understanding of how things work…
4/6/2024
Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras
1
Course Plan
• Introduction to Machine Learning
• What is Machine Learning?
• Types of Machine Learning: Supervised, Unsupervised, Reinforcement
• Real-world applications of Machine Learning
• Supervised Learning
• Linear Regression
• Logistic Regression
• Decision Trees and Random Forests
• Support Vector Machines
• Nearest Neighbour Classifier (kNN)
• Unsupervised Learning
• Clustering (K-Means, Hierarchical)
• Dimensionality Reduction (PCA)
4/6/2024
Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras
2
Course Plan
• Introduction to Neural Networks and Deep Learning
• What are Neural Networks?
• Activation Functions
• Feedforward and Backpropagation
• Introduction to Deep Learning
• Convolutional Neural Networks
• What are CNNs?
• Convolution and Pooling operations
• Use-cases: Image Classification, Object Detection
• Recurrent Neural Networks (RNNs) and Long Short Term Memory
• What are RNNs and LSTMs?
• Use-cases: Time Series Analysis, Natural Language Processing
• Practical Aspects of Machine Learning and Deep Learning
• Overfitting, Underfitting, Bias-Variance Tradeoff
• Regularization Techniques
• Model Evaluation Metrics
• Introduction to popular ML and DL libraries (like scikit-learn, TensorFlow, PyTorch)
4/6/2024
Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras
3
Learning Outcomes
• Understand what machine learning is and its significance.
• Be familiar with different types of machine learning algorithms.
• Recognize real-world applications of machine learning.
• Identify challenges in machine learning and strategies to overcome them.
• Gain insights into the future trends and prospects of machine learning.
Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras
4 4/6/2024
Introduction to Machine Learning
Lecture - 1
Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras
5 4/6/2024
Topics Covered
What is Machine Learning?
Types of Machine Learning: Supervised, Unsupervised, Reinforcement
Real-world applications of Machine Learning
Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras
6 4/6/2024
What is Machine Learning?
Machine Learning is a subset of artificial
intelligence that provides systems the ability to
automatically learn and improve from experience
without being explicitly programmed.
Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras
7 4/6/2024
What is Machine Learning?
● Machine learning is like teaching a child how to ride a
bicycle. Initially, you guide the child by holding the bike,
showing them how to balance. As the child practices, they
learn to balance on their own through trial and error.
Similarly, in machine learning, we provide algorithms with
data and let them learn patterns from it to make predictions
or decisions without being explicitly programmed.
● Imagine having a personal assistant who learns your
preferences over time, suggesting movies you might like or
products you might want to buy. Machine learning enables
such personalized experiences and powers various
applications like recommendation systems, medical
diagnosis, fraud detection, and autonomous vehicles.
Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras
8 4/6/2024
History of Machine Learning
The roots of machine learning can be traced back to the 1950s when scientists began exploring
ways to enable computers to learn from data. Over the decades, with advancements in
computing power and algorithms, machine learning has evolved significantly.
Think of machine learning as a journey from simple rule-based systems to complex neural
networks mimicking the human brain. It has evolved from early perceptrons to deep learning
models capable of processing massive amounts of data with remarkable accuracy.
Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras
9 4/6/2024
Types of Machine Learning
Machine learning can be broadly categorized into supervised, unsupervised, reinforcement,
semi-supervised, and self-supervised learning. Each type serves a unique purpose and has its
own set of algorithms and applications.
Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras
10 4/6/2024
Supervised Learning
● Supervised learning is akin to learning with a teacher. You provide the algorithm with
labeled data, where each example is paired with the correct answer. The algorithm
learns to map inputs to outputs based on this guidance.
● Imagine teaching a child different shapes by showing them pictures and telling them the
names. Similarly, in supervised learning, we train algorithms to recognize patterns, such
as identifying spam emails, predicting house prices, or classifying images into
categories.
● Task of predicting a real number is called regression. (Type - I)
● Task of predicting a categorical output or class is called as classification. (Type - II)
Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras
11 4/6/2024
Supervised Learning
x y
1 10
2 20
3 30
4 ??
Regression
Classification
Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras
12 4/6/2024
Example
import numpy as np
from sklearn.linear_model import LinearRegression
X = np.array([[1], [2], [3], [4]])
y = [10,20,30,40]
reg = LinearRegression().fit(X, y)
prediction = reg.predict(np.array([[10]]))
print("Prediction for input:", prediction)
https://www.kaggle.com/code/pavanmohan/ppt1-example
Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras
13 4/6/2024
Unsupervised Learning
● Unsupervised learning is like exploring a new city without a map or guide. Here, the
algorithm is given unlabeled data and must find patterns or structures on its own.
● Think of clustering similar documents, grouping customers based on purchasing behavior,
or identifying topics in a collection of articles. Unsupervised learning helps in discovering
hidden patterns or insights within data.
● Task of grouping similar objects or entities is called clustering. (Type - I)
● Task of finding associations is called association rules. (Type - II) (customer buys bread,
they are also likely to buy milk)
Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras
14 4/6/2024
Unsupervised Learning
Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras
15 4/6/2024
Labelled
Unlabelled
Reinforcement Learning
Reinforcement learning is comparable to teaching a dog new tricks through rewards and
punishments. The algorithm learns to take actions in an environment to maximize cumulative
rewards. (Ex: Computer in a Tic-tac-toe game)
Consider training an AI to play chess or control a robot. The AI receives feedback in the form of
rewards or penalties based on its actions, guiding it towards better decision-making strategies
over time.
Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras
16 4/6/2024
Reinforcement Learning
/ punishment
Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras
17 4/6/2024
Semi-Supervised and Self-Supervised Learning
Semi-supervised learning is like learning with a mix of labeled and unlabeled data, while self-
supervised learning is akin to learning without explicit supervision, where the algorithm
generates its own labels.
Semi-supervised learning utilizes both labeled and unlabeled data to improve model
performance, whereas self-supervised learning leverages inherent structures or relationships
within the data to generate labels automatically.
Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras
18 4/6/2024
Semi-Supervised and Self-Supervised Learning
Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras
19 4/6/2024
Real-World Applications of Machine Learning
Machine learning finds applications across diverse fields such as healthcare (diagnosis and
drug discovery), finance (fraud detection and algorithmic trading), marketing (customer
segmentation and recommendation systems), and transportation (autonomous vehicles and
traffic prediction).
Create an account in Kaggle and check datasets and other information
https://www.kaggle.com/
Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras
20 4/6/2024
Challenges in Machine Learning
Challenges in machine learning include data scarcity, bias and fairness issues (Ex: more
samples related to a single category), interpretability of models, scalability, and ethical concerns
surrounding privacy and security.
Strategies to address these challenges involve collecting diverse and representative data,
designing fair and transparent algorithms, ensuring model interpretability, and implementing
robust privacy-preserving techniques.
Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras
21 4/6/2024
Future of Machine Learning
Write 10 problem statements implementing supervised (7 Examples), Unsupervised (2
Examples) and Reinforcement (1 Example) in any discipline in which you are interested. -
[Home task]
Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras
22 4/6/2024

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Introduction to Machine Learning Part1.pptx

  • 1. Introduction to Machine Learning and Deep Learning Towards a light-weight understanding of how things work… 4/6/2024 Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras 1
  • 2. Course Plan • Introduction to Machine Learning • What is Machine Learning? • Types of Machine Learning: Supervised, Unsupervised, Reinforcement • Real-world applications of Machine Learning • Supervised Learning • Linear Regression • Logistic Regression • Decision Trees and Random Forests • Support Vector Machines • Nearest Neighbour Classifier (kNN) • Unsupervised Learning • Clustering (K-Means, Hierarchical) • Dimensionality Reduction (PCA) 4/6/2024 Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras 2
  • 3. Course Plan • Introduction to Neural Networks and Deep Learning • What are Neural Networks? • Activation Functions • Feedforward and Backpropagation • Introduction to Deep Learning • Convolutional Neural Networks • What are CNNs? • Convolution and Pooling operations • Use-cases: Image Classification, Object Detection • Recurrent Neural Networks (RNNs) and Long Short Term Memory • What are RNNs and LSTMs? • Use-cases: Time Series Analysis, Natural Language Processing • Practical Aspects of Machine Learning and Deep Learning • Overfitting, Underfitting, Bias-Variance Tradeoff • Regularization Techniques • Model Evaluation Metrics • Introduction to popular ML and DL libraries (like scikit-learn, TensorFlow, PyTorch) 4/6/2024 Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras 3
  • 4. Learning Outcomes • Understand what machine learning is and its significance. • Be familiar with different types of machine learning algorithms. • Recognize real-world applications of machine learning. • Identify challenges in machine learning and strategies to overcome them. • Gain insights into the future trends and prospects of machine learning. Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras 4 4/6/2024
  • 5. Introduction to Machine Learning Lecture - 1 Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras 5 4/6/2024
  • 6. Topics Covered What is Machine Learning? Types of Machine Learning: Supervised, Unsupervised, Reinforcement Real-world applications of Machine Learning Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras 6 4/6/2024
  • 7. What is Machine Learning? Machine Learning is a subset of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras 7 4/6/2024
  • 8. What is Machine Learning? ● Machine learning is like teaching a child how to ride a bicycle. Initially, you guide the child by holding the bike, showing them how to balance. As the child practices, they learn to balance on their own through trial and error. Similarly, in machine learning, we provide algorithms with data and let them learn patterns from it to make predictions or decisions without being explicitly programmed. ● Imagine having a personal assistant who learns your preferences over time, suggesting movies you might like or products you might want to buy. Machine learning enables such personalized experiences and powers various applications like recommendation systems, medical diagnosis, fraud detection, and autonomous vehicles. Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras 8 4/6/2024
  • 9. History of Machine Learning The roots of machine learning can be traced back to the 1950s when scientists began exploring ways to enable computers to learn from data. Over the decades, with advancements in computing power and algorithms, machine learning has evolved significantly. Think of machine learning as a journey from simple rule-based systems to complex neural networks mimicking the human brain. It has evolved from early perceptrons to deep learning models capable of processing massive amounts of data with remarkable accuracy. Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras 9 4/6/2024
  • 10. Types of Machine Learning Machine learning can be broadly categorized into supervised, unsupervised, reinforcement, semi-supervised, and self-supervised learning. Each type serves a unique purpose and has its own set of algorithms and applications. Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras 10 4/6/2024
  • 11. Supervised Learning ● Supervised learning is akin to learning with a teacher. You provide the algorithm with labeled data, where each example is paired with the correct answer. The algorithm learns to map inputs to outputs based on this guidance. ● Imagine teaching a child different shapes by showing them pictures and telling them the names. Similarly, in supervised learning, we train algorithms to recognize patterns, such as identifying spam emails, predicting house prices, or classifying images into categories. ● Task of predicting a real number is called regression. (Type - I) ● Task of predicting a categorical output or class is called as classification. (Type - II) Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras 11 4/6/2024
  • 12. Supervised Learning x y 1 10 2 20 3 30 4 ?? Regression Classification Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras 12 4/6/2024
  • 13. Example import numpy as np from sklearn.linear_model import LinearRegression X = np.array([[1], [2], [3], [4]]) y = [10,20,30,40] reg = LinearRegression().fit(X, y) prediction = reg.predict(np.array([[10]])) print("Prediction for input:", prediction) https://www.kaggle.com/code/pavanmohan/ppt1-example Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras 13 4/6/2024
  • 14. Unsupervised Learning ● Unsupervised learning is like exploring a new city without a map or guide. Here, the algorithm is given unlabeled data and must find patterns or structures on its own. ● Think of clustering similar documents, grouping customers based on purchasing behavior, or identifying topics in a collection of articles. Unsupervised learning helps in discovering hidden patterns or insights within data. ● Task of grouping similar objects or entities is called clustering. (Type - I) ● Task of finding associations is called association rules. (Type - II) (customer buys bread, they are also likely to buy milk) Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras 14 4/6/2024
  • 15. Unsupervised Learning Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras 15 4/6/2024 Labelled Unlabelled
  • 16. Reinforcement Learning Reinforcement learning is comparable to teaching a dog new tricks through rewards and punishments. The algorithm learns to take actions in an environment to maximize cumulative rewards. (Ex: Computer in a Tic-tac-toe game) Consider training an AI to play chess or control a robot. The AI receives feedback in the form of rewards or penalties based on its actions, guiding it towards better decision-making strategies over time. Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras 16 4/6/2024
  • 17. Reinforcement Learning / punishment Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras 17 4/6/2024
  • 18. Semi-Supervised and Self-Supervised Learning Semi-supervised learning is like learning with a mix of labeled and unlabeled data, while self- supervised learning is akin to learning without explicit supervision, where the algorithm generates its own labels. Semi-supervised learning utilizes both labeled and unlabeled data to improve model performance, whereas self-supervised learning leverages inherent structures or relationships within the data to generate labels automatically. Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras 18 4/6/2024
  • 19. Semi-Supervised and Self-Supervised Learning Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras 19 4/6/2024
  • 20. Real-World Applications of Machine Learning Machine learning finds applications across diverse fields such as healthcare (diagnosis and drug discovery), finance (fraud detection and algorithmic trading), marketing (customer segmentation and recommendation systems), and transportation (autonomous vehicles and traffic prediction). Create an account in Kaggle and check datasets and other information https://www.kaggle.com/ Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras 20 4/6/2024
  • 21. Challenges in Machine Learning Challenges in machine learning include data scarcity, bias and fairness issues (Ex: more samples related to a single category), interpretability of models, scalability, and ethical concerns surrounding privacy and security. Strategies to address these challenges involve collecting diverse and representative data, designing fair and transparent algorithms, ensuring model interpretability, and implementing robust privacy-preserving techniques. Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras 21 4/6/2024
  • 22. Future of Machine Learning Write 10 problem statements implementing supervised (7 Examples), Unsupervised (2 Examples) and Reinforcement (1 Example) in any discipline in which you are interested. - [Home task] Pavan Mohan Neelamraju - pavanmohann.github.io - Indian Institute of Technology Madras 22 4/6/2024