Lecture-1-Motivation & Applications

1. Machine Learning Techniques & its Applications
(CAP535)
Motivation & Applications
Lecture-1
6BICTL School of Computing
Data + Algorithms + Computing
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Machine Learning

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Machine Learning

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Machine Learning
Machine Learning- More Refined Definition

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Machine Learning
Applications
• Email Spam Detection
• Image Recognition
• Recommendation Systems
• Autonomous Vehicles
• Medical Diagnosis
Classroom Exercise- Relate these applications to Tom Mitchell’s Definition of ML- 5 Minutes

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Machine Learning
Example-1

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Machine Learning
Example-2

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Machine Learning
Example-3

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Machine Learning
Example-4

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Machine Learning
Example-5

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Machine Learning
Traditional Programming
Rule Based --🡪Follows explicit instruction
Deterministic-🡪 Same input always produces same output
Program Centric--🡪 Known Rules and Logic
Limited Adaptability----🡪Cannot modify their behaviour
(Unless you change the rule)
Machine learning Programming
Data Driven -🡪Learns from Data
Non-Deterministic-🡪 Outputs can vary based on data
Model Centric--🡪 Models that can learn from data(predict)
Adaptability and Learning--🡪Refine their models with data

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Machine Learning
Traditional Programming Vs Machine Learning Programming

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Machine Learning

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Machine Learning
You can see hidden structure when the
dimensions are reduced from 3 to 2
No labels and No feedback
Labels and Feedback are given

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Machine Learning
5 Steps for approaching a Machine Learning application Problem
• Define the problem to be solved
• Collect the Data( Labelled or unlabelled)
• Choose an Algorithm class
• Choose an Optimization metric for learning the model
• Choose a metric for evaluating the model

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What you will learn in this course ?
• Linear Regression
• Linear Classification
• Regularization
• Performance metrics
• Probabilistic Generative and Discriminative models
• PCA and SVD Algorithms for unsupervised mode
• Support Vector Machines
• Neural Networks
• Deep Learning concepts
• Convolutional Neural Networks
• Hidden Markov Models
• Recurrent Neural Networks-LSTM-GRU

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Evaluation:
Internal Examination-Best two out of three CIA Exams –40 Marks(20 +20) + 10 Marks(Assignment)
External Examination – End of semester - 50 Marks
Question Pattern for CIA
Part –A ( Answer any three our of four Questions)= 3 x 10 =30 Marks
Part-B ( One compulsory Question) = 1 x 20= 20 Marks
Semester Exam Pattern
Part-A( Answer any four out of Six questions) = 4 x 20 = 80 Marks
Part-B ( One compulsory Question) = 1 x 20 = 20 Marks

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Assignments: 5 + 5 = 10 Marks
https://matlabacademy.mathworks.com/details/machine-learning-onramp/machinelearning
https://matlabacademy.mathworks.com/details/deep-learning-onramp/deeplearning

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References:
1.Pattern Recognition and Machine Learning by Christopher M. Bishop
2.Introduction to Statistical Learning by Gareth James, Daniela Witten,
Trevor Hastie, and Robert Tibshirani
3.Hands-On Machine Learning with Scikit-Learn, Keras, and
TensorFlow by Aurélien Géron
4.Gilbert Strang- Linear Algebra and its applications
5.Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron
Courville
6.Machine Learning: A Probabilistic Perspective by Kevin P. Murphy
7.Python Machine Learning by Sebastian Raschka and Vahid Mirjalili

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Thank you !