History of Artificial Intelligence
Introduction to Artificial Intelligence and Machine
Learning
• Artificial Intelligence (AI) is the simulation of human intelligence in
machines that are programmed to think like humans and mimic
their actions.
• Machine Learning (ML) is a subset of AI that provides systems the
ability to automatically learn and improve from experience
without being explicitly programmed.
Limitations of Deep Learning
• Deep learning models require large amounts of data and
computational power.
• They can be prone to overfitting and may not generalize well to
new data.
Basic Structure of a Perceptron
• A perceptron is the simplest type of artificial neural network.
• It consists of a single layer of neurons that take in multiple inputs,
apply weights to them, and output a single binary value.
Turing Test
• The Turing Test is a measure of a machine's ability to exhibit
intelligent behavior equivalent to, or indistinguishable from, that
of a human.
,House Pricing Index Prediction: Linear Regression
Linear regression is a statistical method that allows us to study
relationships between two continuous variables.
• It is commonly used for prediction and forecasting tasks, such as
predicting house prices based on various features.
Linear Regression: Mean Squared Error and Root Mean
Squared Error in Model Accuracy
• Mean squared error (MSE) is a common metric used to evaluate
the accuracy of a regression model.
• Root mean squared error (RMSE) is the square root of the mean of
the square of all of the error.
Decision Trees: Inferring Prediction Rules from Data to
Make Decisions
• A decision tree is a type of supervised learning algorithm that is
mostly used in classification problems.
• It works for both categorical and continuous input and output
variables.
Classification Algorithms: An Overview
• Classification is a process in machine learning that sorts input data
into categories.
• There are many types of classification algorithms, including
logistic regression, decision trees, and support vector machines.
,Q-Learning Algorithm: Reinforcement learning method
• Q-learning is a value-based algorithm in reinforcement learning.
• It uses a table to guide the agent to the best action at each state.
Policy-Based Learning: Focus on choosing optimal policies
• Policy-based methods are a class of algorithms that use a policy
to decide on the next action.
• They are well suited for continuous action spaces.
K-Means: A clustering algorithm utilizing iterative process
with starting centroids for categorizing data points based
on their distances.
• K-means is a type of unsupervised learning algorithm used for
clustering.
• The algorithm assigns each data point to one of K groups based
on the distances between the points and the centroids of the
groups.
Elbow Method: Optimal K-value finding technique
employed for K-means and KNN for minimizing distortion
and choosing the number of clusters.
• The elbow method is a way of estimating the optimal number of
clusters in a dataset.
• It involves plotting the within-cluster sum of squares vs the
number of clusters and looking for the "elbow" in the plot.
, Machine Learning Algorithms and Models
• Machine learning algorithms are used to train models on data.
• Once trained, the models can be used to make predictions on new
data.
Importance of Feature Extraction in Machine Learning
• Feature extraction is the process of transforming raw data into a
set of features that can be used to train machine learning models.
• It is an important step in the machine learning pipeline as it can
help to improve model performance and reduce overfitting.
Random Forest: Algorithm & Explanation
• Random Forest is a popular machine learning algorithm that
belongs to the supervised learning technique.
• It can be used for both Classification and Regression problems in
ML.
Naive Bayes: Algorithm & Mathematical Explanation
• Naive Bayes is a classification technique based on Bayes' Theorem
with an assumption of independence between predictors.
• In simple terms, a Naive Bayes classifier assumes that the presence
of a particular feature in a class is unrelated to the presence of
any other feature.
Why AI has gained importance now
• Recent advances in computing power, data availability, and
algorithmic development have contributed to the increased
importance of AI.
Introduction to Artificial Intelligence and Machine
Learning
• Artificial Intelligence (AI) is the simulation of human intelligence in
machines that are programmed to think like humans and mimic
their actions.
• Machine Learning (ML) is a subset of AI that provides systems the
ability to automatically learn and improve from experience
without being explicitly programmed.
Limitations of Deep Learning
• Deep learning models require large amounts of data and
computational power.
• They can be prone to overfitting and may not generalize well to
new data.
Basic Structure of a Perceptron
• A perceptron is the simplest type of artificial neural network.
• It consists of a single layer of neurons that take in multiple inputs,
apply weights to them, and output a single binary value.
Turing Test
• The Turing Test is a measure of a machine's ability to exhibit
intelligent behavior equivalent to, or indistinguishable from, that
of a human.
,House Pricing Index Prediction: Linear Regression
Linear regression is a statistical method that allows us to study
relationships between two continuous variables.
• It is commonly used for prediction and forecasting tasks, such as
predicting house prices based on various features.
Linear Regression: Mean Squared Error and Root Mean
Squared Error in Model Accuracy
• Mean squared error (MSE) is a common metric used to evaluate
the accuracy of a regression model.
• Root mean squared error (RMSE) is the square root of the mean of
the square of all of the error.
Decision Trees: Inferring Prediction Rules from Data to
Make Decisions
• A decision tree is a type of supervised learning algorithm that is
mostly used in classification problems.
• It works for both categorical and continuous input and output
variables.
Classification Algorithms: An Overview
• Classification is a process in machine learning that sorts input data
into categories.
• There are many types of classification algorithms, including
logistic regression, decision trees, and support vector machines.
,Q-Learning Algorithm: Reinforcement learning method
• Q-learning is a value-based algorithm in reinforcement learning.
• It uses a table to guide the agent to the best action at each state.
Policy-Based Learning: Focus on choosing optimal policies
• Policy-based methods are a class of algorithms that use a policy
to decide on the next action.
• They are well suited for continuous action spaces.
K-Means: A clustering algorithm utilizing iterative process
with starting centroids for categorizing data points based
on their distances.
• K-means is a type of unsupervised learning algorithm used for
clustering.
• The algorithm assigns each data point to one of K groups based
on the distances between the points and the centroids of the
groups.
Elbow Method: Optimal K-value finding technique
employed for K-means and KNN for minimizing distortion
and choosing the number of clusters.
• The elbow method is a way of estimating the optimal number of
clusters in a dataset.
• It involves plotting the within-cluster sum of squares vs the
number of clusters and looking for the "elbow" in the plot.
, Machine Learning Algorithms and Models
• Machine learning algorithms are used to train models on data.
• Once trained, the models can be used to make predictions on new
data.
Importance of Feature Extraction in Machine Learning
• Feature extraction is the process of transforming raw data into a
set of features that can be used to train machine learning models.
• It is an important step in the machine learning pipeline as it can
help to improve model performance and reduce overfitting.
Random Forest: Algorithm & Explanation
• Random Forest is a popular machine learning algorithm that
belongs to the supervised learning technique.
• It can be used for both Classification and Regression problems in
ML.
Naive Bayes: Algorithm & Mathematical Explanation
• Naive Bayes is a classification technique based on Bayes' Theorem
with an assumption of independence between predictors.
• In simple terms, a Naive Bayes classifier assumes that the presence
of a particular feature in a class is unrelated to the presence of
any other feature.
Why AI has gained importance now
• Recent advances in computing power, data availability, and
algorithmic development have contributed to the increased
importance of AI.
