About this Course

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Advanced Level

Course requires strong background in calculus, linear algebra, probability theory and machine learning.

Approx. 33 hours to complete
English

Skills you will gain

Bayesian OptimizationGaussian ProcessMarkov Chain Monte Carlo (MCMC)Variational Bayesian Methods

Learner Career Outcomes

38%

started a new career after completing these courses

24%

got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Advanced Level

Course requires strong background in calculus, linear algebra, probability theory and machine learning.

Approx. 33 hours to complete
English

Offered by

Placeholder

National Research University Higher School of Economics

Syllabus - What you will learn from this course

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Week
1

Week 1

3 hours to complete

Introduction to Bayesian methods & Conjugate priors

3 hours to complete
10 videos (Total 57 min), 3 readings, 2 quizzes
10 videos
Think bayesian & Statistics review7m
Bayesian approach to statistics5m
How to define a model3m
Example: thief & alarm11m
Linear regression10m
Analytical inference3m
Conjugate distributions2m
Example: Normal, precision5m
Example: Bernoulli4m
3 readings
About the University10m
Rules on the academic integrity in the course10m
MLE estimation of Gaussian mean10m
2 practice exercises
Introduction to Bayesian methods30m
Conjugate priors30m
Week
2

Week 2

7 hours to complete

Expectation-Maximization algorithm

7 hours to complete
17 videos (Total 168 min)
17 videos
Probabilistic clustering6m
Gaussian Mixture Model10m
Training GMM10m
Example of GMM training10m
Jensen's inequality & Kullback Leibler divergence9m
Expectation-Maximization algorithm10m
E-step details12m
M-step details6m
Example: EM for discrete mixture, E-step10m
Example: EM for discrete mixture, M-step12m
Summary of Expectation Maximization6m
General EM for GMM12m
K-means from probabilistic perspective9m
K-means, M-step7m
Probabilistic PCA13m
EM for Probabilistic PCA7m
2 practice exercises
EM algorithm30m
Latent Variable Models and EM algorithm30m
Week
3

Week 3

2 hours to complete

Variational Inference & Latent Dirichlet Allocation

2 hours to complete
11 videos (Total 98 min)
11 videos
Mean field approximation13m
Example: Ising model15m
Variational EM & Review5m
Topic modeling5m
Dirichlet distribution6m
Latent Dirichlet Allocation5m
LDA: E-step, theta11m
LDA: E-step, z8m
LDA: M-step & prediction13m
Extensions of LDA5m
2 practice exercises
Variational inference15m
Latent Dirichlet Allocation15m
Week
4

Week 4

6 hours to complete

Markov chain Monte Carlo

6 hours to complete
11 videos (Total 122 min)
11 videos
Sampling from 1-d distributions13m
Markov Chains13m
Gibbs sampling12m
Example of Gibbs sampling7m
Metropolis-Hastings8m
Metropolis-Hastings: choosing the critic8m
Example of Metropolis-Hastings9m
Markov Chain Monte Carlo summary8m
MCMC for LDA15m
Bayesian Neural Networks11m
1 practice exercise
Markov Chain Monte Carlo30m

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About the Advanced Machine Learning Specialization

Advanced Machine Learning

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