#2.1 Learning strategies #2.2 Learning a Class #2.3 S, G, and the Version Space #2.4 VC Dimension #2.5 Multiple Classes #2.6 Regression

#3.1 Probability and Inference #3.2 Bayes’ Rule #3.3 Losses and Risks

#4.1 largest margin #4.2 Hypothesis class #4.3 Apriori algorithm #4.4 support & confidence #4.5 support & confidence-1 #4.6 KNN #4.7 Gradient Descent #4.8 Decision Trees #4.9 Kernel smoother #4.10 واجب 2 - Colab #4.11 mid2-Q2 #4.12 mid2-Q3 #4.13 mid2Q5 #4.14 K-means #4.15 2 K-means /example #4.16 Decision Trees #4.17 Naive bayes #4.18 support & confidence-2 #4.19 تمارين شبتر2 #4.20 تمارين شبتر2 #4.21 Naive Bayesian Classifier #4.22 Training Examples -1 #4.23 Training Examples -2 #4.24 Forward Propagation Example #4.25 mid1-bayes rule #4.26 mid1-مقارنه #4.27 probably approximatly-mid1 #4.28 أختياري-1 mid #4.29 neuron forward

#5.1 Histogram #5.2 Naive estimator

#6.1 Decision Trees #6.2 Entropy of a Joint Distribution #6.3 Specific Conditional Entropy #6.4 Conditional Entropy #6.5 Information Gain #6.6 Revisiting Our Original Example #6.7 Constructing Decision Trees

#7.1 Linear Regression-1 #7.2 Linear Regression-2 #7.3 Gradientdescent #7.4 Regularization

#8.1 Neural Networks -1 #8.2 Logisticunit #8.3 Examples #8.4 Multi-class classification #8.5 Backpropagation example

#9.1 K-‐means #9.2 PCA vs FA

#10.1 Decision Tree Learning #10.2 Decision Tree for PlayTennis #10.3 Which Attribute is ”best”?

#11.1 Non-parametric methods #11.2 k-NN density estimation