Artificial Intelligence Lec2 PDF

# ARTIFICIAL INTELLIGENCE ## Agenda - Machine Learning and its Applications ## Supervised Learning Algorithms - Machine Learning - Supervised Learning - Classification - Decision Tree - Discriminant Analysis - Naive Bayes - Logistic Reg...

# ARTIFICIAL INTELLIGENCE ## Agenda - Machine Learning and its Applications ## Supervised Learning Algorithms - Machine Learning - Supervised Learning - Classification - Decision Tree - Discriminant Analysis - Naive Bayes - Logistic Regression - Support Vector Machine - Regression - Linear Regression - SVR - Regression Tree - Ensemble Methods - GLM - Unsupervised Learning When the output variable is categorical or discrete, such as good or bad, yes or no, prone to disease or not prone to disease the problem that we need to solve is a classification problem. When the output variable is a real value that is continuous, such as age, weight or price, the problem that we need to solve is a regression problem. - Please Note: Logistic regression is not a regression algorithm. ## Unsupervised Learning Algorithms - Machine Learning - Supervised Learning - Unsupervised Learning - Clustering - Hierarchical - K-means - Hidden Markov Model - Gaussian Mixture - Fuzzy C-means - Association We use clustering model when we want to discover the inherent groupings in the data. We use association rule when we want to discover rules that describe large portions of our data. Using association rules, we discover relationships between variables in a large dataset. ## Supervised Learning Process: Two Steps - **Learning (training):** Learn a model using the training data. - **Testing:** Test the model using unseen test data to assess the model accuracy. - **Accuracy:** No. of correct classifications / Total no. of test cases *Diagram illustrating the steps involved in supervised learning. This diagram shows the flow of data from training data to the model, then from the model to test data, and finally from test data to accuracy.* ## Supervised Learning Supervised learning problems can be further grouped into regression and classification problems. - **Classification:** A classification problem is when the output variable is a category, such as "red" or "blue" or "disease" and "no disease". - **Regression:** A regression problem is when the output variable is a real value, such as "dollars" or "weight". ## List of Common Supervised Machine Learning Algorithms - Decision Trees - K Nearest Neighbors - Linear SVC (Support vector Classifier) - Logistic Regression - Linear Regression ## Advantages of Supervised Learning - It allows you to be very specific about the definition of the labels. - You are able to determine the number of classes you want to have. - The input data is very well known and is labeled. - The results produced by the supervised method are more accurate. ## Disadvantages of Supervised Learning - Supervised Learning can be a complex method. - Supervised Learning needed a lot of computation time for training. - If you have dynamic big and growing data, you are not sure of the labels to predefine the. ## Unsupervised Learning Unsupervised learning is where you only have input data (X) and no corresponding output variables. The goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data. These are called unsupervised learning because unlike supervised learning above there is no correct answers and there is no teacher. Algorithms are left to their own devises to discover and present the interesting structure in the data. *Diagram showing the flow of data from input raw data to algorithm, followed by interpretation and processing, and finally to output.* ## Unsupervised Learning Unsupervised learning problems can be further grouped into clustering and association problems. - **Clustering:** A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behavior. - **Association:** An association rule learning problem is where you want to discover rules that describe large portions of your data, such as people that buy X also tend to buy Y. ## List of Common Supervised Machine Learning Algorithms - K-means clustering - K-NN (K nearest neighbors) - Dimensionality Reduction - Hierarchical clustering ## Advantages of Supervised Learning - Less complexity in comparison with supervised learning. - It is often easier to get unlabeled data. - Takes place in real time such that all the input data to be analyzed and labeled in the presence of learners. ## Disadvantages of Supervised Learning - You cannot get very specific about the definition of the data sorting and the output. - Less accuracy of the results. - The results of the analysis cannot be ascertained.