This was an Online Lecture Describing Key Concepts of Machine Learning Strategies inclusing Neural Networks National Webinar On Education 4.0 “Ensuring Continuity in Learning and Innovation Through Digitization” Organized By: Singhad Institute of Management, Pune in Association with Savitribai Phule Pune University 12th June 2020

1. Prof. Amlan Chakrabarti
IEEE Computer Soc. Dist. Vist. & ACM Dist. Speaker
Director, A.K.Choudhury School of Information Technology
University of Calcutta
National Webinar On Education 4.0 “Ensuring Continuity in Learning and Innovation Through Digitization”
Organized By: Singhad Institute of Management, Pune in Association with Savitribai Phule Pune University
12th June 2020

2. INTRODUCTORY CONCEPTS

3. Formal Definition
• “Machine learning is the field of study which gives the computers the ability to
learn without being explicitly programmed”- Arther Samuels 1959
• “A computer program is said to learn from experience E with respect to some
class of tasks T and performance measure P, if its performance at tasks in T, as
measured by P, improves with experience E.”- Tom Mitchells 1997
• Machine learning focuses on the development of computer programs that can
access data and use it learn for themselves.
• Example: To predict, traffic patterns at a busy intersection (task T)
• We can run it through a machine learning algorithm with data about past
traffic patterns (experience E)
• If it has successfully “learned”, it will then do better at predicting future traffic
patterns (performance measure P).

4. Defining The Learning Task

5. Why Machine Learning is Different?

6. Evolution of Machine Learning

7. Real World Problems lead to ML
• The goal of ML is never to make “perfect” guesses, the goal is to
make guesses that are good enough to be useful

8. Deep Learning in The Headlines

9. Deep Learning
9
Basics
• A deep neural network consists of a hierarchy of layers, whereby each
layer transforms the input data into more abstract representations
(e.g. edge -> nose -> face)
• The output layer combines those features to make predictions

10. Scene Labeling Using DL

11. Machine Learning for Smarter World

12. MACHINE LEARNING STRATEGIES

13. Machine Learning Techniques
• Supervised Learning
– Regression
– Classification
• Binary
• Multiclass
• Multi-label
• Unsupervised Learning
– Partitional
– Hierarchical
• Reinforcement
• Semi-Supervised

14. Supervised Learning

15. Supervised Learning: Regression
 There are a few concepts to unpack here:
• Dependent Variable
• Independent Variable(s)
• Slope & Intercept
• Error Function

16. Supervised Learning: Classification
Binary Classification
Multi-label Classification
Multiclass Classification

17. Unsupervised Learning
• No labels are given to the learning algorithm, leaving it on its own to
find structure in its input
• The goal of unsupervised learning is to find hidden patterns in
unlabeled data

18. Unsupervised Learning: Clustering
• Finding groups of objects such that objects in a group are similar (or
related) to one another and different from (or unrelated to) the objects in
other groups
Partitional Clustering Hierarchical Clustering

19. Applications of Clustering

20. Reinforcement Learning
• A technique to allow an agent to take actions and interact with an
environment so as to maximize the total rewards
• Similar to toddlers learning how to walk who adjust actions based on the
outcomes they experience
• A Playing Agent
– Manages to score a point it gets a +1 reward
– Each time it loses a point it gets a -1 penalty.
– it will iteratively update its policies so that the actions that bring rewards
are more probable and those resulting in a penalty are filtered out.
• The first application in which reinforcement learning gained notoriety was
when AlphaGo, a machine learning algorithm, won against one of the world’s
best human players in the game Go

21. Semi Supervised Learning
• Supervised Learning algorithm is a costly process, especially when
dealing with large volumes of data
• Unsupervised Learning is that it’s application spectrum is limited.
• Semi-supervised learning combines a small amount of labeled data
with a large amount of unlabeled data during training
• Application Scenarios: Speech Analysis, Internet Content
Classification, Protein Sequence Classification

22. Designing A Learning System
• Choose the training experience
• Choose exactly what is to be learned
– i.e. the target function
• Choose how to represent the target function
• Choose a learning algorithm to infer the target function from the
experience

23. NEURAL NETWORKS

24.  Massively parallel interconnected network of simple processing elements
which are intended to interact with the objects of the real world in the
same way as biological systems do.
 NN models are extreme simplifications of human neural systems.
Neural Networks

25. 25

26. 26

27.  In context of Machine Learning, we want to learn the parameters from the
training set, such that given the testing set data the training set can
correctly classify the instances
Artificial Neural Network (ANN)

28. ANN: Role of Weights & Bias
Adjusting Weights Adjusting Bias

29. ANN: Understanding the Layers

30. ANN: Forward & Backward Propagation

31. The Big Picture

32. ML in Practice

33. ML Platforms
Online Platforms Python Libraries

34. Future of Machine Learning
• As ML assumes increased importance in business applications, this
technology will be offered as a Cloud-based service known as Machine
Learning-as-a-Service (MLaaS)
• Connected AI systems will enable ML algorithms to “continuously learn,”
based on newly emerging information on the internet
• There will be a big rush among hardware vendors to enhance system
power to accommodate ML data processing. More accurately, hardware
vendors will be pushed to redesign their machines to do justice to the
powers of ML
• ML will help machines to work autonomously sense of context and
meaning of data