### Slides

#### Part I: Machine Learning Ideas

01: Introduction

02: K-nearest neighbors / Evaluation

03: Cross-Validation

04: Simple Linear Regression (Linear Regression I)

05: Multiple Linear Regression (Linear Regression II)

06: Regularized Regression (Linear Regression III)

#### Part II: Probability and Statistics / Models for Supervised Learning

07-08: Probability / Random Variables (Probability I and II)

09-10: Expectation / Random Vectors (Probability III and IV)

10-11: Max Likelihood and the Linear Model

12: Bias Variance Tradeoff in Supervised Learning

13: Bias and Variance in Linear Regression

14: Bayesian Inference I

15: Bayesian Inference II

16: Bayesian Inference III

17-18: Naive Bayes Classification

19: Bayesian Regression / Bayesian Occam’s Razor

#### Part III: Approximation Methods

20: Logistic Regression With Newton-Raphson (Approximate Inference I)

21: Laplace Approximation (Approximate Inference II)

22: Sampling (Approx. III)

#### Part IV: Models with Latent Variables (Unsupervised Learning)

23: K-Means (Clustering I)

24: Mixture of Gaussians and EM (Clustering II)

25: Theory Behind EM (Clustering III)

26: Bayesian Clustering / Gibbs Sampling (Clustering IV)

27-28: Markov Chain Monte Carlo

29: Belief Networks / Graphical Models

30: Belief Networks / Graphical Models II

31: Forward-Backward Algorithm (Hidden Markov Models I)

32: EM for HMMs (HMMs II)

33: Gibbs Sampling for HMMs (HMMs III)

#### Part V: Bayesian Nonparametric Models

34-35: Gaussian Process Regression

36: Gaussian Process Classification

37-38: Infinite Mixture Model (Nonparametric Clustering)