Lecture 18 part 04 - Examples of Reinforcement Learning Systems

Lecture 18 part 03 - Formulating Reinforcement Learning (part 2: Value Functions)

Lecture 17 part 03 - Vectors, Spaces, Norms, Orthogonality, and Independence

Lecture 16 part 06 - Intuition for Mixture of Gaussians

Lecture 16 part 04 - K-Means Objective, Loss, and Challenges

Lecture 15 part 04 - Maximum Likelihood Estimation for Gaussian Distributions

Lecture 15 part 03 - Maximum Likelihood Estimation for Bernoulli Distributions

Lecture 14 part 07 - Chaining Recurrent Neural Cells

Lecture 13 part 02 - N-Gram Language Models

Lecture 12 part 04 - History of Neural Architectures

Lecture 11 part 05 - Intuition for Convolutions Operation

Lecture 10 part 03 - Solving XOR using Neural Networks

Lecture 09 part 04 - Biological, Computational, and Artificial Neurons

Lecture 08 part 04 - Formulating Max-margin Classification

Lecture 07 part 03 - Linear Classification via Sigmoidal Activations

Lecture 06 part 06 - On the Existance and Identification of Nonlinear Transformations

Lecture 06 part 04 - Precision and Recall

Lecture 05 part 08 - Gradient Descent for Linear Regression

Lecture 04 part 03 - Overview of Ensembling Methods & Discussion

Lecture 04 part 02 - the Bias-Variance Tradeoff

Lecture 03 part 07 - Uncertainty Quantification, Entropy, & Information Gain

Lecture 03 part 06 - Intuition for Learning Decision Trees

Lecture 02 part 07 - Lp Norms and Cosine Similarity

Lecture 01 part 07 - a Brief History of Machine Learning

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