Machine Learning Models

Artificial Intelligence: Types of Learning Models

• Machine Learning: focuses on using appropriate features to build models that accomplish specific tasks.
• Types of Learning Models: classified into three main categories:
  • Logical Models: use logical expressions to construct decision rules based on logical conditions.
  • Geometric Models: use geometry of the instance space to classify data points.
  • Probabilistic Models: use probability theory to classify data points.

Probabilistic Models

• Definition: view features and target variables as random variables.
• Characteristics: model and manipulate the level of uncertainty associated with these variables.
• Approach: differs from k-nearest neighbors and logical models.

Two Types of Probabilistic Models

• Predictive Models: focus on conditional probability distribution P(Y|X) to predict the target variable Y.
• Generative Models: estimate the joint probability distribution P(Y, X) to derive any conditional or marginal distribution involving Y and X.

Unveiling the Joint Distribution

• Joint Probability Distribution: captures the relationship between two variables.
• Generative Models: excel at predicting new data points by leveraging the joint distribution.

Naïve Bayes

• Definition: a popular probabilistic classifier.
• Algorithm: leverages Bayes' rule to determine the likelihood of a specific class.

Grouping and Grading

• Grouping Models: break the instance space into groups or segments and apply a simple method (e.g., decision tree, KNN).
• Grading Models: form a global model over the instance space (e.g., linear classifiers, neural networks).

Designing a Learning System

• Three Elements: T (Task), P (Performance Measure), and E (Training Experience).
• Objective: find an unknown target function that represents the exact knowledge to be learned from the training experience.

Type of Training Experience

• Direct Experience: each board state and the correct move are provided.
• Indirect Experience: sequences of moves and final results are given.
• Credit Assignment Problem: determining how to assign credit or blame to individual moves.

Choosing the Target Function

• Direct Experience: ChooseMove function, which chooses the best move.
• Indirect Experience: Value function, which estimates the quality of the board state.

Choosing a Representation for the Target Function

• Options: large tables, rule sets, polynomial functions, neural networks.
• Characteristics: should be expressive yet manageable with available training data.

Choosing an Approximation Algorithm for the Target Function

• Need for Approximation: the true objective function may be complex or difficult to calculate directly.
• Error Reduction: reduces the difference between predicted values and actual values over time.
• Types of Approximation Algorithms: LMS (Least Mean Square), Time Difference (TD) Learning, and others.

Final Design for the Checkers Learning System

• Four Modules: Performance System, Critic, Generalizer, and Experiment Generator.
• Modules' Roles: performance system plays games, critic generates training examples, generalizer learns from examples, and experiment generator creates new problems.

Supervised Learning

• Definition: learning from examples with labeled data.
• Goal: create a function that maps new inputs to corresponding outputs.
• Types of Problems: classification and regression.

Unsupervised Learning

• Definition: learning from unlabeled data.
• Goals: discover hidden structures and patterns, estimate the probability distribution of the data.
• Techniques: density estimation, clustering, and others.

Reinforcement Learning

• Definition: trial and error learning with rewards.

• Agent's Goal: maximize the cumulative reward by taking actions in an environment.

• Reward System: acts as a critic, providing feedback on the effectiveness of the agent's actions.### Reinforcement Learning

• The goal is to learn a policy that maximizes long-term reward

• The agent interacts with the environment, takes actions, and receives rewards based on those actions

• The learning process involves exploring different actions and favoring those that lead to higher rewards

• Example: Training a dog, where the dog receives positive reinforcement (treats) for desired behaviors and no reward for undesired ones

Real-World Applications of Reinforcement Learning

• Controlling robot movements
• Scheduling tasks for optimal efficiency
• Recommendation systems where the "reward" could be user satisfaction

Comparison with Supervised Learning

• Reinforcement learning operates through trial and error, guided by the reward system
• Supervised learning involves learning from labeled examples provided by an expert

Perspectives in Machine Learning

• Machine learning can be viewed as a search problem through a vast space of possibilities
• The core idea is that machine learning algorithms navigate a vast space of potential solutions (hypotheses) to find the one that best fits the training data and any existing knowledge

Hypothesis Space

• The space encompasses all possible models or functions the learner can create
• Example: Training an AI to play checkers, where the hypothesis space includes all possible evaluation functions

Learning Algorithms as Search Tools

• The LMS algorithm (or other learning algorithms) act as search tools within the hypothesis space
• They iteratively refine the model based on the difference between the model's predictions and the actual training data

Importance of Hypothesis Representation

• The choice of how the model is represented significantly impacts the search process
• Different representations are suitable for learning different types of functions

Search Strategies and Underlying Structure

• Each learning algorithm leverages the specific structure of the chosen hypothesis space to guide its search

Issues in Machine Learning

• Effective learning algorithms: What kind of algorithms are best suited to learn general patterns from specific training examples?
• Convergence and training data: How much training data is required for an algorithm to reliably converge on the desired function?
• Generalizability and prior knowledge: Ensuring the learned model performs well on unseen data and incorporating existing knowledge into the learning process
• Active learning and strategy: Can the learning algorithm actively choose the most informative training examples to improve its learning efficiency?
• Function approximation and representation: Choosing the best type of functions for the learning model and automating function selection within the learning algorithm
• Representation learning: Can the learning algorithm automatically adjust its internal representation to improve its ability to learn the target function?### Introduction to Python
• Python is a modern, high-level language that is easy to learn and use.
• It has a simple syntax, making it a great language for beginners.
• Python supports Object-Oriented Programming (OOP) and has a dynamic and incremental build process.

Reserved Words in Python

• Reserved words in Python are predefined words that cannot be used as variable names.
• These words are used to perform specific functions in Python.

How to Run Python

• To run Python, you need to install it from the official website: https://www.python.org/.
• You can use a text editor to write Python code and save it with a .py extension.
• To run the code, open a terminal or command prompt, navigate to the directory where the file is saved, and type python filename.py.

Interactive Mode in Python

• Python has an interactive mode that allows you to write and execute code one line at a time.
• To enter interactive mode, type python in the terminal or command prompt.
• This mode is useful for testing and debugging code.

Data Types in Python

• Python has several built-in data types, including:
  • Integers (int): whole numbers, e.g., 123, 456.
  • Floating-point numbers (float): decimal numbers, e.g., 3.14, -0.5.
  • Complex numbers (complex): numbers with real and imaginary parts, e.g., 3+4j, -2-5j.
  • Strings (str): sequences of characters, e.g., "hello", 'hello'.

Variables in Python

• Variables are used to store values in Python.
• A variable is a name given to a value, and it can be used to perform operations on that value.
• Variables are case-sensitive, and they can be reassigned to a new value.

Input and Output in Python

• Input is the process of getting data from the user, and output is the process of displaying data to the user.
• The print() function is used to display output to the user.
• The input() function is used to get input from the user.

Printing Output in Python

• The print() function is used to display output to the user.
• Output can be a string, a number, or a combination of both.
• The print() function can be used to display multiple values separated by commas.

Getting Input in Python

• The input() function is used to get input from the user.
• Input is always a string, and it needs to be converted to a different data type if necessary.
• The input() function can be used to get multiple values separated by commas.

Assigning Values to Variables

• The assignment operator (=) is used to assign a value to a variable.
• The value on the right-hand side of the operator is assigned to the variable on the left-hand side.
• Variables can be reassigned to a new value.