2. DIY/Component-Based Solutions (e.g., ADS1299 + Arduino/ESP32) This is the "bare bones" approach and the most budget-friendly if you are willing to invest a lot of time and effort. When to choose a DIY solution: This is the best option if your mini-project is the circuit itself. If you want to learn about analog front-end design, PCB layout, and low-level programming (e.g., configuring an ADS1299 chip), then this is the way to go. It offers the lowest hardware cost but the highest time and knowledge investment. This is not recommended for a beginner who wants to focus on the application (e.g., controlling a game with brain signals) rather than the hardware.

1. Dept. of ECE, Vemana IT 1
Presentation
On
“Inductive bias – CO4”
By
Batch No: 07
MEGHANA B N 1VI24EC409
Prof . SUMA B V
Dept. of ECE
2025-26

2. Dept. of ECE, Vemana IT 2
Inductive bias – CO4
 What is Inductive bias?
• Inductive bias in machine learning refers to the set of assumptions that a
learning algorithm uses to predict outputs for inputs it has not
encountered, enabling generalization beyond the training data.
• Inductive bias helps the algorithm focus on what's important in the data
and avoid getting lost in irrelevant details.
• It steers the learning process towards specific types of solutions.
• Exmples:-
X C(x)
1 1
2 0
3 1

3. Dept. of ECE, Vemana IT 3
 Inductive bias: definition
 Given:
-a concept learning algorithm L for a set of instances X
-a concept e defined over X
-a set of training examples for c: Dc = {(x, c(x))}
-L(xi, Dc) outcome of classification of xi, after learning.
 Inductive inference (>):
Dc ^ xi >L(xi, Dc)
 The inductive bias is defined as a minimal set of assumptions
B, such that (|- for deduction)
∀(xi X) [(B^Dc ^x
∈ i ) |- L(xi, Dc)]

4. Dept. of ECE, Vemana IT 4
 A Biased Hypothesis Space
Example: Sky AirTemp Humidity Wind Water Forecast
EnjoySport
• No hypothesis consistent with the three examples with the assumption
that the target is a conjunction of constraints
• <?, Warm, Normal, Strong, Cool, Change) is too general
• Target concept exists in a different space H', including disjunction and
in particular the hypothesis
• Sky=Sunny or Sky=Cloudy

5. Dept. of ECE, Vemana IT 5
Inductive and Deductive Reasoning
Inductive Reasoning
• Specific to General
• Logically true
• May or may not be realistically
true
Example:
• St1: Mango is fruit(Specific)
• St2: The box is full of
fruits(Specific)
• Con: The box is full of
mangoes(General)
Deductive Reasoning
• General to Specific
• Logically true
• Realistically true
Example:
• Stl: All mangoes are fruits.
(General)
• St2: All fruits have seeds.
(General)
• Con: Mangoes have seeds.
(Specific)

6. Dept. of ECE, Vemana IT 6
Inductive System

7. Dept. of ECE, Vemana IT 7
Equivalent deductive System

8. Dept. of ECE, Vemana IT 8

9. Dept. of ECE, Vemana IT 9
THANK YOU

10. Dept. of ECE, Vemana IT 10