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  • Counting

  • Multi-variable Linear Regression

  • Matrix Decomposition

  • Cost Minimization using Gradient Descent

  • Overview of Sparse Modeling

  • Simple Linear Regression with Tensorflow

  • Sets

  • Linear models in Deep Neural Networks

  • Convexity of Linear Hypothesis and Margin Bound of Linear Classifiers

  • Maximum Margin Principle and Soft Margin Hard Margin

  • Perceptron and its convergence theorem

  • Introduction to Probability and Statistics

  • Correlation and Experimental Design

  • More Distributions and the Central Limit Theorem

  • Random Numbers and Probability