Machine Learning techniques used in Artificial Intelligence- Supervised, Unsupervised, Reinforcement Learning. It discusses about Linear Regression, Logistic Regression, SVM, Random forest, KNN, K-Means Clustering and Apriori Algorithm. It also Illustrates the applications of AI in various fields.

1. MACHINE LEARNING
TECHNIQUES IN ARTIFICIAL
INTELLIGENCE
- T.ARCHANA
ASSISTANT PROFESSOR
COMPUTER SCIENCE AND ENGINEERING
SRM INSTITUTE OF SCIENCE AND TECHNOLOGY

2. AGENDA
• About AI
• AI vs ML vs DL
• Machine learning
• Types of machine learning
• Supervised learning
• Unsupervised Learning
• Reinforcement Learning
• Algorithms used in ML
• Use cases of ML

3. ABOUT AI
• AI is a technique that enables machines to mimic human behavior. Artificial
Intelligence is the theory and development of computer systems able to
perform tasks normally requiring human intelligence.

4. WHY ARTIFICIAL INTELLIGENCE?
• With the help of AI, you can create such software or devices which can solve
real-world problems very easily and with accuracy such as health issues,
marketing, traffic issues, etc.
• With the help of AI, you can create your personal virtual Assistant, such as
Cortana, Google Assistant, Siri, etc.
• With the help of AI, you can build such Robots which can work in an
environment where survival of humans can be at risk.
• AI opens a path for other new technologies, new devices, and new
Opportunities.

5. APPLICATIONS OF AI

6. AI VS ML VS DL
• AI- Artificial Intelligence is defined as a field of science and engineering that
deals with making intelligent machines or computers to perform human-like
activities.
• ML- Machine Learning is defined as the branch of Artificial Intelligence and
computer science that focuses on learning and improving the performance
of computers/machines through past experience by using algorithms.
• DL - Deep Learning is a set of algorithms inspired by the structure and
function of the human brain. It uses a huge amount of structured as well as
unstructured data to teach computers and predicts accurate results.

8. MACHINE LEARNING
• Subset of AI provides the machine to learn automatically and improve from
experience without being explicitly programmed
• A Machine Learning algorithm is a set of rules and statistical techniques used
to learn patterns from data and draw significant information from it.

9. EXAMPLE

10. TYPES OF MACHINE LEARNING

11. SUPERVISED LEARNING
• Supervised learning is a method in which we teach the machine using
labelled data.
• Supervised Learning is the process of making an algorithm to learn to map an
input to a particular output. This is achieved using the labelled datasets that
you have collected.
• If the mapping is correct, the algorithm has successfully learned.

14. PROBLEM AIM ALGORITHMS
APPLICATIONS
TYPE

15. UNSUPERVISED LEARNING
• The machine is trained on unlabelled data without any guidance
• The goal of unsupervised learning is to find the underlying structure of
dataset, group that data according to similarities, and represent that dataset in
a compressed format.

18. PROBLEM AIM ALGORITHMS
APPLICATIONS
TYPE

19. REINFORCEMENT LEARNING
• An agent interacts with its environment by producing actions and discovers
errors and rewards
• Reinforcement Learning is a feedback-based Machine learning technique in
which an agent learns to behave in an environment by performing the actions
and seeing the results of actions. For each good action, the agent gets positive
feedback, and for each bad action, the agent gets negative feedback or
penalty.
• The agent learns with the process of hit and trial, and based on the experience

21. PROBLEM AIM ALGORITHMS APPLICATIONS
TYPE

22. RISKS OF MACHINE LEARNING
• Poor Data
• Overfitting
• Biased data
• Lack of strategy and experience
• Security Risks
• Data privacy and confidentiality
• Third-party risks
• Regulatory challenges

23. ALGORITHMS USED IN ML
SUPERVISED
LEARNING
Linear Regression
Logistic Regression
SVM
K- Nearest Neighbour
Random Forest
Decision Tree
UNSUPERVISED
LEARNING
K- Means
Apriori
C- Means
REINFORCEMENT
LEARNING
Q- Learning
SARSA
(State Action Reward State
Action)

24. LINEAR REGRESSION
• It is a statistical method that is used for predictive analysis.
• Linear regression makes predictions for continuous/real or numeric variables such
as sales, salary, age, product price, etc.
• Linear regression algorithm shows a linear relationship between a dependent (y)
and one or more independent (y) variables, hence called as linear regression.
• The linear regression model provides a sloped straight line representing the
relationship between the variables.
• It finds how the value of the dependent variable is changing according to the
value of the independent variable.

25. Mathematically, we can represent a linear regression as:
y= a0+a1x+ ε
Here,
Y= Dependent Variable (Target Variable)
X= Independent Variable (predictor Variable)
a0= intercept of the line (Gives an additional degree of freedom)
a1 = Linear regression coefficient (scale factor to each input value).
ε = random error
The values for x and y variables are training datasets for Linear
Regression model representation.

26. LINEAR REGRESSION USE CASES
• Sales Forecasting
• Risk Analysis
• Housing Applications To Predict the prices and other factors
• Finance Applications To Predict Stock prices, investment evaluation, etc.

27. LOGISTIC REGRESSION
• Logistic Regression analysis predicts the outcome in a binary variable which
has only two possible outcomes.
• It is a technique to analyse a data-set which has a dependent variable and one
or more independent variables to predict the outcome in a binary variable,
meaning it will have only two outcomes.
• The dependent variable is categorical in nature.

28. Linear regression equation:
y = β0 + β1X1 + β2X2 …. + βnXn
•Y stands for the dependent variable that needs to be predicted.
•β0 is the Y-intercept, which is basically the point on the line which
touches the y-axis.
•β1 is the slope of the line (the slope can be negative or positive
depending on the relationship between the dependent variable and the
independent variable.)
•X here represents the independent variable that is used to predict our
resultant dependent value.

29. LOGISTIC REGRESSION USE CASES
• Weather Prediction
• Determining Illness

30. SVM
• It is used for Classification problems in Machine Learning.
• The goal of the SVM algorithm is to create the best line or decision boundary
that can segregate n-dimensional space into classes so that we can easily put
the new data point in the correct category in the future. This best decision
boundary is called a hyperplane.
• SVM chooses the extreme points/vectors that help in creating the hyperplane.
These extreme cases are called as support vectors, and hence algorithm is
termed as Support Vector Machine.

31. To select the maximum hyperplane in
the given sets, the support vector
machine follows the following sets:
•Generate hyperplanes which
segregates the classes in the best
possible way
•Select the right hyperplane with the
maximum segregation from either
nearest data points

32. SUPPORT VECTOR MACHINE USE CASES
• Face Detection
• Text And HyperText Categorization
• Classification Of Images
• Bioinformatics
• Protein Fold and Remote Homology Detection
• Handwriting Recognition
• Generalized Predictive Control

33. KNN
• K nearest neighbors or KNN Algorithm is a simple algorithm which uses the
entire dataset in its training phase. Whenever a prediction is required for an
unseen data instance, it searches through the entire training dataset for k-most
similar instances and the data with the most similar instance is finally
returned as the prediction.
• K-NN algorithm stores all the available data and classifies a new data point
based on the similarity.

34. How to Choose the K- value?
• You can use cross validation technique
to test several values of k and choose
the best or which suits the best.
• The distance is measeured using
• Euclidean distance
• Manhattan distance

35. KNN USE CASES
• Amazon – Recommed Systems
• Concept Search

36. RANDOM FOREST
• The algorithm randomly creates a forest with several trees.
• The higher the number of trees in the forest, greater is the accuracy of the
results.
• Random forest builds multiple decision trees (called the forest) and glues
them together to get a more accurate and stable prediction. The forest it
builds is a collection of Decision Trees, trained with the bagging method.

37. The dataset is divided into subsets and
given to each decision tree. During the
training phase, each decision tree
produces a prediction result, and when
a new data point occurs, then based on
the majority of results, the Random
Forest classifier predicts the final
decision.

39. RANDOM FOREST USE CASES
• Banking: Banking sector mostly uses this algorithm for the identification of
loan risk.
• Medicine: With the help of this algorithm, disease trends and risks of the
disease can be identified.
• Land Use: We can identify the areas of similar land use by this algorithm.
• Marketing: Marketing trends can be identified using this algorithm.

40. K-MEANS CLUSTERING
• k-means clustering is one of the simplest algorithms which uses unsupervised
learning method to solve known clustering issues. k-means clustering require
following two inputs.
1.k = number of clusters
2.Training set(m) = {x1, x2, x3,……….., xm}

41. HOW DOES THE K-
MEANS ALGORITHM
WORK?
Step-1: Select the number K to decide the number
of clusters.
Step-2: Select random K points or centroids. (It can
be other from the input dataset).
Step-3: Assign each data point to their closest
centroid, which will form the predefined K clusters.
Step-4: Calculate the variance and place a new
centroid of each cluster.
Step-5: Repeat the third steps, which means
reassign each datapoint to the new closest centroid
of each cluster.
Step-6: If any reassignment occurs, then go to
step-4 else go to FINISH.

42. K-MEANS CLUSTERING USE CASES
• Customer Segmentation
• Identifying Crime Localities
• Document Classification
• Diagnostic systems
• Search engines
• Wireless sensor networks

43. APRIORI ALGORITHM
• The Apriori algorithm uses frequent itemsets to generate association rules,
and it is designed to work on the databases that contain transactions. With the
help of these association rule, it determines how strongly or how weakly two
objects are connected.
• Frequent itemsets are those items whose support is greater than the threshold
value or user-specified minimum support.

44. • Support: It gives the fraction of transactions which contains item A and B.
Basically Support tells us about the frequently bought items or the
combination of items bought frequently.
• Confidence: It tells us how often the items A and B occur together, given the
number times A occurs.

45. Step-1: Determine the support of itemsets in the transactional
database, and select the minimum support and confidence.
Step-2: Take all supports in the transaction with higher
support value than the minimum or selected support value.
Step-3: Find all the rules of these subsets that have higher
confidence value than the threshold or minimum confidence.
Step-4: Sort the rules as the decreasing order

46. APRIORI ALGORITHM USE CASES
• Education Field: Extracting association rules in data mining of admitted
students through characteristics and specialties.
• Medical field: For example Analysis of the patient’s database.
• Forestry: Analysis of probability and intensity of forest fire with the forest
fire data.
• Apriori is used by many companies like Amazon in the Recommender
System and by Google for the auto-complete feature.

47. USE CASES OF ML

48. USE CASES OF ML

49. USE CASE- IMAGE & VIDEO RECOGNITION
Companies using image
& video
recognition: Google,
Shutterstock, Pinterest,
eBay, Salesforce, Yelp,
Apple, Amazon, Facebook.

50. USE CASE- SPEECH RECOGNITION
• Speech recognition is used in search
engines (e.g. Google, Baidu), virtual
digital assistants
(i.e. Alexa, Cortana, Siri, Google
Assistant, AliGenie), smart speakers
(e.g. Amazon Echo, Google Home),
and voice-activated applications
(e.g. Uber, Evernote).

51. USE CASE- HEALTH CARE
• Machine Learning for healthcare and
bioinformatics can process a massive
amount of data and deliver valuable
insights that can help healthcare
professionals in making quick decisions.
• It also allows them to analyze a patient’s
medical history and predict the outcomes
based on their treatment and lifestyle.

52. USE CASE - FINANCE
• Machine Learning is being used in the finance
industry to make businesses automated and
more secure.
• It is used for Financial Monitoring ,Process
automation, Secure transaction, Risk
management, Algorithmic trading, Financial
regulators and advisory, Customer data
management, Decision making and
investment prediction, Customer service
improvement, Customer retention program,
Marketing