The document presents a machine learning presentation by five students. It discusses key machine learning concepts including supervised learning (classification and regression), unsupervised learning (clustering and association), semi-supervised learning, and reinforcement learning. Examples of applications are provided. The differences between traditional computer science programs and machine learning programs are outlined. The future of machine learning is predicted to include its integration into all AI systems, machine learning-as-a-service, continuously learning connected systems, and hardware enhancements to support machine learning capabilities.

1. TO
OUR PRESENTATION

2. PORT CITY
INTERNATIONAL
UNIVERSITY,
CHITTAGONG
CSE-13BATCH (A)

3. PRESENTED BY-
1. Farhan Nasir (CSE 01306125)
2. Md Mominul Hoque (CSE 01306104)
3. Subashis Roy Bowmik (CSE 01306114)
4. Pushpita Shil (CSE 01306100)
5. Maqsudur Rahman()

4. OUR TOPIC :-
MACHINE LEARNING

5. Discussion
1.What is Machine Learning?
2.Supervised Machine Learning
3.Unsupervised Machine Learning
4.Semi Supervised Machine Learning
5.Reinforcement Machine Learning
6.Uses Of Machine Learning
7.Difference Between CSE Program & Machine
Learning Program
8.Future Of Machine Learning

6. What is Machine?

7. What is Machine Learning?
- Capable to analyse data
- Ability to learn itself
- Take decision

8. Supervised Machine Learning
-pre define data
-train data
-understanding the data
-take decision
Y = f(X)
input variables = (x)
output variable = (Y)
Supervised Learning can be divided into 2 categories
1.Classification
2.Regression

9. Classification: A classification problem is when the output variable is a
category, such as “red” or “blue” or “disease” and “no disease”.
Example: Spam Detection, Churn Prediction, Sentiment Analysis, Dog
Breed Detection.
Regression: A regression problem is when the output variable is a real
value, such as “dollars” or “weight”.
Example: House Price Prediction, Stock Price Prediction, Height-
Weight Prediction.

10. Unsupervised Machine Learning
-only have input data (X)
-unlabeled data
-no corresponding output variables
Unsupervised learning problems can be further grouped into clustering and
association problems.
Clustering: A clustering problem is where you want to discover the inherent
groupings in the data, such as grouping customers by purchasing behavior.

11. Association: An association rule learning problem is where you want
to discover rules that describe large portions of your data, such as
people that buy X also tend to buy Y.
Example: Given many baskets (could be text documents, actual
supermarket baskets, other semi-structured objects) find which items
inside a basket predict another item in the basket.

12. Semi Supervised Machine Learning
=Supervised + Unsupervised

13. Reinforcement Machine Learning
-interacts with its environment
-produce actions
-trail
-discovers errors
-automatically determine
the ideal behavior
- maximize its performance

14. Uses Of Machine Learning
• Image recognition
• Voice recognition
• Predictions
• Spam and Malware
• Search engine, Google Allo, Map, Facebook, Gmail, Youtube,
Twitter etc.
• Fraud Detection

15. Difference Between CSE Program &
Machine Learning Program
CSE Program
-make program
-give input
-known output
ML Program
-give data
-data access or analyse
-known/unknown output
-making program itself
-take decision

16. Future Of Machine Learning
Some predictions about Machine Learning based on current technology
trends and ML’s systematic progression toward maturity:
• ML will be an integral part of all AI systems, large or small.
• Cloud-based service known as Machine Learning-as-a-Service.
• Connected AI systems will enable ML algorithms to “continuously
learn,” based on newly emerging information on the internet.
• A big rush among hardware vendors to enhance CPU power to
accommodate ML data processing.
• Hardware vendors will be pushed to redesign their machines to do
justice to the powers of ML.
• Machine Learning will help machines to make better sense of
context and meaning of data.

17. Mind it, I’m not GOOGLE,
Write Down Questions For you!
But You May Ask Few !!