The document discusses a machine learning course at CHRIST University. It includes the course objectives, outcomes, plan, textbook, and topics to be covered. The course aims to provide an introduction to machine learning algorithms and their applications. Key topics will include origins of machine learning, supervised and unsupervised learning methods like KNN, Naive Bayes, decision trees, and neural networks. Students will learn to evaluate model performance and prepare data for machine learning models. Labs will provide hands-on experience applying machine learning algorithms to datasets.

1. MISSION
CHRIST is a nurturing ground for an individual’s
holistic development to make effective contribution to
the society in a dynamic environment
VISION
Excellence and Service
CORE VALUES
Faith in God | Moral Uprightness
Love of Fellow Beings
Social Responsibility | Pursuit of Excellence
MACHINE LEARNING – BCA671
Dr. Ramesh Chandra Poonia
Associate Professor
Department of Computer Science
CHRIST (Deemed to be University), Bangalore, Karnataka, INDIA - 560029
Email: rameshchandra.poonia@christuniversity.in
Web: https://rameshcpoonia.com/

2. Excellence and Service
CHRIST
Deemed to be University
Agenda
● Course Objective
● Course Outcomes
● Course Plan
● Course Overview with example
● ML (with some definitions)

3. Excellence and Service
CHRIST
Deemed to be University
Course Objective
The objective of this course is to provide introduction to the
principles and applications of machine learning algorithms.

4. Excellence and Service
CHRIST
Deemed to be University
Course Outcomes
At the end of the course, students will be able to
● CO1: Understand the basic principles of machine learning
models
● CO2: Evaluate and prepare data for machine learning models
● CO3: Evaluate the performance of machine learning models

5. Excellence and Service
CHRIST
Deemed to be University
● Course Plan

6. Excellence and Service
CHRIST
Deemed to be University
Text Book
Exercises : In Python & its libraries
[T1] Brett Lantz, Machine Learning with R:
Expert techniques for predictive modeling,
3rd Edition, Packt Publishing, 2019.

7. Excellence and Service
CHRIST
Deemed to be University
Recommended References
[1] K. P. Murphy, Machine Learning: A Probabilistic Perspective. MIT Press,2012.
[2] P. Harrington, Machine Learning in Action. Manning Publications,2012
[3] C. M. Bishop, Pattern Recognition and Machine Learning. Springer,2016.
[4] S. Marsland, Machine Learning: An Algorithmic Perspective. 1st Ed. Chapman
and Hall, 2009
Web Resource: http://www.cs.princeton.edu/courses/archive/spr08/cos511/

8. Excellence and Service
CHRIST
Deemed to be University
What We’ll Cover in this Course
● UNIT- I: Origins of machine learning, how machines learn and types
of machine learning algorithms.
● UNIT- II: Lazy Learning - Why is KNN algorithm Lazy and Probabilistic
Learning - Classification using Naïve Bayes
● UNIT-III: Divide and conquer, the C5.0 algorithm
● UNIT-IV: Forecasting numeric data, black box methods
● UNIT-V: Finding groups of data - understanding clustering
● UNIT-VI: Evaluating model performance

9. Excellence and Service
CHRIST
Deemed to be University
Lab Plan
Lab No. Lab Objective
1 Demonstrate data Exploration (Numeric Data)
2 Demonstrate data Exploration (Categorical and Nominal Data)
3 Demonstrate KNN classification
4 Demonstrate Naïve Bayes classification
5 Demonstrate classification using decision Tree
6 Demonstrate simple linear regression
7 Demonstrate data clustering
8 Demonstrate classification using MLP
https://drive.google.com/drive/folders/12AJxSUeCL51FZt7Hf05-f0vFd4KpChZp

11. Excellence and Service
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Deemed to be University
A Few Quotes
● “A breakthrough in machine learning would be worth ten
Microsofts” (Bill Gates, Chairman, Microsoft)
● “Machine learning is the next Internet” (Tony Tether, Director,
DARPA)
● “Web rankings today are mostly a matter of machine learning”
(Prabhakar Raghavan, Dir. Research, Yahoo)
● “Machine learning is going to result in a real revolution” (Greg
Papadopoulos, CTO, Sun)
● “Machine learning is today’s discontinuity” (Jerry Yang, CEO,
Yahoo)

13. Asynchronous Task
1. What you think about machine learning?
2. What is your research area ?
3. Find out some applications as per your research area and write a
summary on this.

15. Excellence and Service
CHRIST
Deemed to be University
UNIT-I
● Origins of Machine Learning- Uses and abuses of machine learning
● Machine learning successes - limits of machine learning - machine
learning ethics- data storage
● Abstraction – generalization – evaluation - How machines learn
● Machine Learning in practice – types of input data – types of machine
learning algorithms – matching input data to algorithms
● Exploring and understanding data – exploring the structure of data –
exploring numeric variables
● Exploring categorical variables – exploring the relationship among
variables

16. Excellence and Service
CHRIST
Deemed to be University
Session Content
● Origins of Machine Learning
● Application areas of Machine Learning
● Why Machine Learning is important?
● What is Machine Learning?
● Design a Learning System
● Issues in Machine Learning

17. Excellence and Service
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Deemed to be University
Think about Artificial Intelligence

18. AI as a discipline

19. Artificial Intelligence VS Machine Learning VS Deep Learning

20. Artificial Intelligence VS Machine Learning VS Deep Learning

21. Artificial Intelligence VS Machine Learning VS Deep Learning

22. Excellence and Service
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Deemed to be University
Machine learning
● Machine learning is a scientific discipline that explores the
construction and study of algorithms that can learn from data.
● Such algorithms operate by building a model based on inputs and
using that to make predictions or decisions, rather than following only
explicitly programmed instructions.

23. Excellence and Service
CHRIST
Deemed to be University
Traditional Programming
Machine Learning
Computer
Data
Program
Output
Computer
Data
Output
Program

24. Contents

26. Why Study Machine Learning?
● Gartner predicts that by 2022
there would be at least 40% of
new application development
project going on in the market
that would be requiring machine
learning co-developers on their
team.

27. Why is Machine Learning Important?
• Some tasks cannot be defined well, except by examples.
• Relationships and correlations can be hidden within large amounts of data. Machine
Learning may be able to find these relationships.
• Human designers often produce machines that do not work as well as desired in the
environments in which they are used.
• The amount of knowledge available about certain tasks might be too large for explicit
encoding by humans (e.g., medical diagnostic).
• New knowledge about tasks is constantly being discovered by humans. It may be
difficult to continuously re-design systems “by hand”.

28. When Do We Use Machine Learning?
ML is used when:
• Human expertise does not exist (navigating on Mars)
• Humans can’t explain their expertise (speech recognition)
• Models must be customized (personalized medicine)
• Models are based on huge amounts of data (genomics)
Learning isn’t always useful:
• There is no need to “learn” to calculate payroll
Slide Credit: Eric Eaton

29. Excellence and Service
CHRIST
Deemed to be University
Session Content
● Application Domains
● Application Types
● Application areas of Machine Learning
● Why Machine Learning is important?
● What is Machine Learning?
● History of Machine learning
● Design a Learning System
● Issues in Machine Learning

30. • Web search
• Computational biology
• Finance
• E-commerce
• Space exploration
• Robotics
• Information extraction
• Social networks
• Language Processing
Many more emerging…
Application Domains

31. State of the Art Applications of Machine Learning
.

32. Excellence and Service
CHRIST
Deemed to be University
Application Types
– Medical diagnosis
– Credit card applications or transactions
– Fraud detection in e-commerce
– Worm detection in network packets
– Spam filtering in email
– Recommended articles in a newspaper
– Recommended books, movies, music, or jokes
– Financial investments
– DNA sequences
– Spoken words
– Handwritten letters
– Astronomical images

33. It is very hard to say what makes a 2
Slide credit: Geoffrey Hinton
Pattern recognition

34. Autonomous Cars
• Nevada made it legal for
autonomous cars to drive
on roads in June 2011
• As of 2013, four states
(Nevada, Florida, California,
and Michigan) have legalized
autonomous cars
UPenn’s Autonomous Car →
Slide credit: Eric Eaton

35. Learning of Object Parts

36. Automatic Speech Recognition
ML used to predict phoneme states from sound
spectrogram Deep Learning Based Results
# Hidden Layers 1 2 4 8 10 12
Word Error Rate % 16.0 12.8 11.4 10.9 11.0 11.1
Baseline Gaussian Mixture Model based word error rate =
15.4%
[Zeiler et al. “On rectified linear units for speech
recognition” ICASSP 2013]
Slide credit: Eric Eaton

37. Excellence and Service
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Deemed to be University
Robotics

38. What is Machine Learning?
Machine Learning (M. L.) is a subset of artificial intelligence (AI) which provides
machines the ability to learn automatically and improve from experience without
being explicitly programmed.

39. What is Machine Learning?

40. Traditional Programming
Machine Learning
Computer
Data
Program
Output
Computer
Data
Output
Program

41. History of Machine learning
● Machine learning came in to existence in late 1980s and early 1990s.
● Problems faced during late 1980s.
○ How to model large statistical models?
○ How to train robust systems of AI systems?
○ How to design operational models of brain?
● The above stated problems led to the existence of Machine Learning.

42. What machine Learning can do?

43. What machine Learning can do?

44. Where does ML fit in?

45. How Machine Learning (Algorithms) works?

46. Data Exploration in Machine Learning
● Data exploration steps to follow before building a machine learning model
include:
● Variable identification: define each variable and its role in the dataset
● Univariate analysis: for continuous variables, build box plots or histograms
for each variable independently; for categorical variables, build bar charts to
show the frequencies
● Bi-variable analysis - determine the interaction between variables by building
visualization tools
• Continuous and Continuous: scatter plots
• Categorical and Categorical: stacked column chart
• Categorical and Continuous: boxplots combined with swarmplots
● Detect and treat missing values
● Detect and treat outliers

47. Data Exploration in Python
• Efficient dataframe object for data manipulation with integrated indexing
• Tools for reading and writing data between disparate formats
• Integrated handling of missing data and intelligent data alignment
• Reshaping of datasets
• Time series-functionality
• Intelligent label-based slicing, fancy indexing, and subsetting of large datasets
• Columns can be inserted and deleted from data structures for size mutability
• Aggregating or transforming data with a powerful group by engine allowing split-
apply-combine operations on datasets
• High performance merging and joining of datasets
• Hierarchical axis indexing

48. Data Exploration in R
• Loading the data: such as .XLS, TXT, CSV, and JSON
• Converting variables
• Transpose a dataset
• Sorting of dataframe: accomplished by using order as an index
• Create plots or histograms
• Generate frequency tables to best understand the distribution across categories
• Generate a sample set with just a few random indices
• Remove duplicate values of a variable
• Find class-level count average and sum: R data exploration techniques include
apply functions to accomplish this
• Recognize and treat missing values and outliers by inputting with the mean of
other numbers
• Merge and join datasets: R includes an appending datasets function and a bind
function

49. Steps of Data Exploration and Preparation
1. Variable Identification:
2. Univariate Analysis:
3. Bi-variate Analysis
4. Missing values treatment
5. Outlier treatment
6. Variable transformation
7. Variable creation

50. ● Variable Identification: First, identify Predictor (Input)
and Target (output) variables. Next, identify the data type
and category of the variables.
https://towardsai.net/p/data-analysis/exploratory-data-analysis-in-python-ebdf643a33f6

51. ● Variable Identification: First, identify Predictor (Input)
and Target (output) variables. Next, identify the data type
and category of the variables.

52. Steps of Data Exploration in Python
1. Importing Libraries:
2. Importing Dataset
3. Identification of data types
4. Size of the dataset
5. Statistical Summary of Numeric Variables
6. Non-Graphical Univariate Analysis: Histogram, Box Plots, Count Plots
7. To get the list & number of unique values
8. Finding null values
● References:
1. https://towardsai.net/p/data-analysis/exploratory-data-analysis-in-python-ebdf643a33f6
2. https://chartio.com/learn/data-analytics/what-is-exploratory-data-analysis/#exploratory-data-analysis-with-chartio

53. ● References:
1. https://colab.research.google.com/drive/1RUwqSh61UDWnM0XAyz9rQ3hQ9U
vKfEEO#scrollTo=CGVVnMsKu_O1
2. https://towardsai.net/p/data-analysis/exploratory-data-analysis-in-python-
ebdf643a33f6
3. https://chartio.com/learn/data-analytics/what-is-exploratory-data-
analysis/#exploratory-data-analysis-with-chartio