This unit introduces the basic concepts and principles of machine learning, a key branch of artificial intelligence that enables systems to learn from data and improve performance without explicit programming. It explains different types of learning methods such as supervised, unsupervised, and reinforcement learning.

1. Unit 4 – Introduction to
Machine Learning
Overview, Algorithms, and Evaluation

2. What is Machine Learning?
• Definition: ML is a subset of AI where
systems learn patterns from data to make
decisions or predictions.
• Key idea: Systems improve automatically with
experience.
• Example: Email spam detection,
recommendation systems.

3. Types of Machine Learning
• Supervised Learning: Models trained with
labeled data.
• Unsupervised Learning: Models find patterns
in unlabeled data.
• Reinforcement Learning: (Optional mention)
Models learn via rewards/punishments.

4. Supervised Learning
• Definition: Learns from input-output pairs.
• Goal: Predict outcomes for new data.
• Examples: Spam detection, loan approval.
• Key tasks:
– Classification: Predict categories
– Regression: Predict continuous values

5. Unsupervised Learning
• Definition: Learns patterns without labeled
outputs.
• Goal: Discover hidden structure in data.
• Examples: Customer segmentation, anomaly
detection.
• Common methods: Clustering, dimensionality
reduction.

6. Classification Overview
• Definition: Assigns data to discrete categories.
• Examples:
– Email: Spam or Not Spam
– Medical: Disease vs. Healthy
• Common algorithms: Logistic Regression,
Decision Trees, Random Forest.

7. Regression Overview
• Definition: Predicts continuous numerical
values.
• Examples:
– House price prediction
– Stock price forecasting
• Common algorithms: Linear Regression,
Ridge, Lasso.

8. Logistic Regression
• Purpose: Used for binary classification.
• Concept: Estimates probability of class
membership using the sigmoid function.
• Formula: P(y=1)=11+e−zP(y=1) = frac{1}
{1+e^{-z}}P(y=1)=1+e−z1​
• Pros & Cons: Simple and interpretable,
struggles with complex boundaries.

9. Decision Trees
• Purpose: Can be used for classification and
regression.
• Concept: Splits data into branches based on
feature thresholds.
• Advantages: Easy to interpret, handles non-
linear relationships.
• Visual: Include a simple tree diagram.

10. Linear Regression
• Purpose: Predicts a continuous dependent
variable.
• Formula: y=β0+β1x1+ +
⋯ βnxn+ϵy = beta_0 +
beta_1 x_1 + dots + beta_n x_n + epsilony=β0​
+β1​
x1​
+ +
⋯ βn​
xn​
+ϵ
• Assumptions: Linearity, independence,
homoscedasticity.
• Example: Predicting salary based on experience.

11. Ridge Regression
• Purpose: Linear regression with L2
regularization.
• Formula: Minimize ∑(y−y^)2+λ∑βi2sum(y -
hat{y})^2 + lambda sum beta_i^2∑(y−y^​
)2+λ∑βi2​
• Key point: Reduces overfitting by shrinking
coefficients.

12. Lasso Regression
• Purpose: Linear regression with L1
regularization.
• Formula: Minimize ∑(y−y^)2+λ∑ β
∣ i sum(y
∣
- hat{y})^2 + lambda sum |beta_i|∑(y−y^​
)2+λ∑ β
∣ i​
∣
• Key point: Can perform feature selection by
setting some coefficients to zero.

13. Model Evaluation Metrics
• Confusion Matrix: True Positive, True
Negative, False Positive, False Negative.
• Metrics derived:
– Accuracy: (TP+TN)/Total
– Precision: TP/(TP+FP)
– Recall: TP/(TP+FN)
– F1-score: Harmonic mean of Precision and Recall.

14. Choosing the Right Algorithm
• Factors to consider:
– Type of problem (Classification vs Regression)
– Data size & quality
– Model interpretability
– Computational resources

15. Summary & Key Takeaways
• ML helps systems learn from data to predict or classify.
• Supervised vs Unsupervised learning: Know the
difference.
• Important algorithms: Logistic Regression, Decision
Trees, Linear Regression, Ridge, Lasso.
• Evaluation metrics ensure models perform well in real
scenarios.
• Encourage practice: “Try building models with real
datasets!”

16. Thank You