Course Weekly Lecture Plan

Week	Topic
1	What is machine learning? - Applications of machine learning - Assessing model accuracy - Regression vs classification - Measuring the quality of fit - The bias-vaariance trade off - Supervised vs unsupervised learning - Introduction to R
2	Simple linear regression - Estimating the coefficients - Accuracy of the estimates - Accuracy of the model - Multiple linear regression - Estimating the multiple regression coefficients - Basics of R notation.
3	Other considerations in the regression model - Qualitative predictors - Extensions of the linear model - Potential problems - Non-parametric regression - K-Nearest Neighbors algorithm - Basic of estimation with R.
4	An overview of classification - Logistic regression - The logistic model - Estimating the regression - Making predictions - Multiple logistic regression - Logistic regression for multiple (>2) response classes - Classification with R.
5	Linear discriminant analysis - Using Bayes’ Theorem for classification - Linear discriminant analysis for multiple (p>1) features - Quadratic discriminant analysis - K-Nearest Neighbors for classification - A comparison of classification methods.
6	Understanding Naive Bayes - Basic concepts of Bayesian methods - Understanding the joint probability notion - computing conditional probability with Bayes Theorem - The Naive Bayes algorithm - The Laplace estimator - Using numeric features with Naive Bayes.
7	Resampling methods - Cross validation - The validation set approach - Leave-One-Out Cross-Validation - k-Fold Cross-Validation - The bootstrap.
8	Shrinkage methods - Ridge regression - The Lasso - Selecting the tuning parameter - Dimension reduction methods - Principal components regression - Partial least squares - Considerations in high dimensions.
9	Tree based methods - Basics of decision trees - Regression trees - Classi?cation trees - Trees vs linear models - Advantages and disadvantages of trees - Bagging - Random forests - Boosting.
10	Support vector classifiers - Maximal margin classifier - What is a hyperplane? - Support vector machines - Classi?cation with non-linear decision boundaries - SVMs with multiple classes - Relationship to logistic regression.
11	Understanding neural networks - From biological to artifical neurons - Activation functions - Network topology - Number of layers - The direction of information travel - The number of nodes in each layer - Training neural networks with backpropagation - Deep learning.
12	Introduction to unsupervised learning - Principal components analysis - What are principal components? - Interpretation of principal components.
13	Clustering methods - K-means clustering - Hierarchical clustering - Practical issues in clustering.
14	Understanding association rules - The Apriori algorithm for association rule learning - Measuring rule interest - Support and confidence - Building a set of rules with the Apriori principle - Market basket analysis.

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