intro

1. Introduction To
Machine
Learning and
Artificial
Intelligence
By
Dr. Gaurav Singal

2. Artificial Intelligence

6. Introduction to Machine Learning
❑Machine learning is a discipline that deals with programming the systems
to make them automatically learn and improve with experience.
❑It is difficult to consider all the decisions based on all possible inputs.
❑To solve this problem, algorithms are developed that build knowledge
from a specific data and past experience by applying the principles of
statistical science, probability, logic, mathematical optimization,
reinforcement learning, and control theory.

7. 7
What is Machine Learning?
•Machine Learning
• Study of algorithms that
• improve their performance
• at some task
• with experience
•Optimize a performance criterion using example data
or experience.
•Role of Statistics: Inference from a sample
•Role of Computer science: Efficient algorithms to
• Solve the optimization problem
• Representing and evaluating the model for inference

8. Traditional Programming v/s Machine
Learning
❑Traditional Programming: Data
and program is run on the
computer to produce the output.
❑Machine Learning: Data and
output is run on the computer to
create a program. This program
can be used in traditional
programming.

9. Machine Learning in Real World
Machine learning is like farming or gardening.

10. Growth of Machine Learning
• Machine learning is preferred approach to
• Speech recognition, Natural language processing
• Computer vision
• Medical outcomes analysis
• Robot control
• Computational biology
• This trend is accelerating
• Improved machine learning algorithms
• Improved data capture, networking, faster computers
• Software too complex to write by hand
• New sensors / IO devices
• Demand for self-customization to user, environment
• It turns out to be difficult to extract knowledge from human experts →
failure of expert systems in the 1980’s.
Alpydin & Ch. Eick: ML Topic1 10

11. Examples of Machine Learning
❑ Web search:
➢ Ranking page based on what you are most
likely to click on.
❑ Computational biology:
➢ Rational design drugs in the computer based
on past experiments.
❑ Finance:
➢ Decide who to send what credit card offers
to. Evaluation of risk on credit offers. How
to decide where to invest money.

12. Applications of Machine Learning

14. Applications of Machine Learning
❑ E-commerce:
➢Predicting customer churn. Whether or not a
transaction is fraudulent.
❑ Space exploration:
➢Space probes and radio astronomy.
❑ Robotics:
➢How to handle uncertainty in new
environments. Autonomous. Self-driving car.
❑ Information extraction:
➢Ask questions over databases across the web.

15. Applications of Machine Learning
❑ Social networks:
➢Data on relationships and preferences.
Machine learning to extract value from
data.
❑ Debugging:
➢Use in computer science problems like
debugging. Labor intensive process.
Could suggest where the bug could be.

16. Steps involved in Machine Learning
DEFINING A
PROBLEM
PREPARING
DATA
EVALUATING
ALGORITHMS
IMPROVING
RESULTS
PRESENTING
RESULTS

17. Key elements
of Machine
Learning
❑ Representation
❑ Evaluation
❑ Optimization

18. Types of
learning
❑ Supervised learning
❑ Unsupervised learning
❑ Semi-supervised
learning
❑ Reinforcement learning

19. Learning Associations
•Basket analysis:
P (Y | X ) probability that somebody who buys X also
buys Y where X and Y are products/services.
Example: P ( chips | beer ) = 0.7
Market-Basket transactions
TID Items
1 Bread, Milk
2 Bread, Diaper, Beer, Eggs
3 Milk, Diaper, Beer, Coke
4 Bread, Milk, Diaper, Beer
5 Bread, Milk, Diaper, Coke

20. Types of learning

21. Supervised learning
❑Model relationships and dependencies between
the target prediction output and the input
features such that we can predict the output
values for new data based on those relationships
which it learned from the previous data sets.
❑The training data includes both Inputs and
Labels (Targets)
❑For example, addition of two numbers a=5,b=6
result =11, Inputs are 5,6 and Target is 11.

22. Types of learning : Supervised learning
• also called inductive learning
• Training data includes desired outputs.

23. Supervised learning: Classification
• A classification problem is when the output variable is a category or a
group, such as “black” or “white” or “spam” and “no spam”.

24. 24
Classification: Applications
•Aka Pattern recognition
•Face recognition: Pose, lighting, occlusion (glasses,
beard), make-up, hair style
•Character recognition: Different handwriting styles.
•Speech recognition: Temporal dependency.
• Use of a dictionary or the syntax of the language.
• Sensor fusion: Combine multiple modalities; eg, visual (lip
image) and acoustic for speech
•Medical diagnosis: From symptoms to illnesse.

25. 25
Face Recognition
Training examples of a person
Test images
AT&T Laboratories, Cambridge UK
http://www.uk.research.att.com/facedatabase.html

26. 26
Prediction: Regression
•Example: Price of a used
car
•x : car attributes
y : price
y = g (x | θ )
g( ) model,
θ parameters
y = wx+w0

27. 27
Regression Applications
•Navigating a car: Angle of the steering wheel.

28. Different Data Analysis Tasks
• Classification
•Assign a category (i.e.,
a class) for a new
instance
• Clustering
•Form clusters (i.e.,
groups) with a set of
instances
• Pattern detection
•Identify regularities (ie,
patterns) in temporal or
spatial data
• Simulation
•Define mathematical
formulas that can generate
data similar to
observations collected
28

29. 29
Supervised Learning: Uses
•Prediction of future cases: Use the rule to predict the
output for future inputs
•Knowledge extraction: The rule is easy to understand
•Compression: The rule is simpler than the data it
explains
•Outlier detection: Exceptions that are not covered by
the rule, e.g., fraud
Example: decision trees tools that create rules

30. Un-Supervised learning
❑Training data does not include desired outputs.
❑Here training data is not structured (contains noisy data, unknown data and
etc..)
❑Example: A random articles from different pages

31. 31
Unsupervised Learning
•Unsupervised learning is a type of machine learning
algorithm used to draw inferences from datasets
consisting of input data without labelled responses.
•Clustering: Grouping similar instances
•Other applications:
• Predicting the weather.
• Calculating the height of a person in the school.
• Summarization.

32. Un-Supervised learning

33. Semi-Supervised learning
❑Training data does not include desired outputs
❑Models are trained on a combination of labeled and
unlabeled data.
❑Benefit:
The process of labeling massive amounts of data for
supervised learning is often prohibitively time-
consuming and expensive.

34. Reinforcement Learning
❑ It is a behavioral learning model.
❑ It receives feedback from the analysis of the data so the user is guided to the best
outcome.
• Input: The input should be an initial state from which the model will start
• Output: There are many possible output as there are variety of solution to a
particular problem
• Training: The training is based upon the input, The model will return a state and
the user will decide to reward or punish the model based on its output.
• The model keeps continues to learn.
• The best solution is decided based on the maximum reward.

35. 35
Reinforcement Learning
•Topics:
• Policies: what actions should an agent take in a particular
situation
• Utility estimation: how good is a state (→used by policy)
•No supervised output but delayed reward
•Credit assignment problem (what was responsible
for the outcome)
•Applications:
• Game playing
• Robot in a maze
• Multiple agents, partial observability, ...

36. REINFORCEMENT
LEARNING
SUPERVISED LEARNING
• Reinforcement learning is all
about making decisions
sequentially. In simple words
we can say that the out
depends on the state of the
current input and the next
input depends on the output of
the previous input
• In Supervised learning the
decision is made on the initial
input or the input given at the
start
• In Reinforcement learning
decision is dependent, So we
give labels to sequences of
dependent decisions
• Supervised learning the
decisions are independent of
each other so labels are given
to each decision.
Example: Chess game Example: Object recognition

37. Gaurav Singal, gauravsingal789@gmail.com
Thank you
Contact me:
gauravsingal789@gmail.com
gaurav.singal@nsut.ac.in
www.gauravsingal.in
Thank You