Adrian - Machine learning tutor - London
1st lesson free
Adrian - Machine learning tutor - London

Adrian's email address and phone number have been validated.

Adrian

  • Rate R1323
  • Response 7h
  • Students

    Number of students Adrian has accompanied since joining Superprof

    3

    Number of students Adrian has accompanied since joining Superprof

Adrian - Machine learning tutor - London
  • 5 (1 review)

R1323/h

1st lesson free

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1st lesson free

1st lesson free

  • Machine learning

Machine learning and data science with explanations and codes for beginners up to computer science or statistics undergraduate and graduate students

  • Machine learning

Lesson location

About Adrian

I have been teaching in top universities for over 20 years and am a senior professor. I have taught and/or authored papers on these topics. You will receive dedicated teaching, full explanations and help instructions on how to implement the methods you learn. Let me know your level and the lessons can be designed accordingly, either for pure beginners or more advanced learners.

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About the lesson

  • Primary
  • Secondary
  • Matric/GCSE
  • +14

    • levels :

    Primary

    Secondary

    Matric/GCSE

    AS Level

    A Level

    BTech

    Adult education

    Undergraduate

    Masters

    Diploma

    Doctorate

    MBA

    Beginner

    Intermediate

    Advanced

    Professional

    Kids

  • English

Languages in which the lesson is available :

English

Do you want to understand machine learning either as a beginner or an intermediate/advanced student? These lessons can start at the appropriate level, depending on your existing knowledge. Included for free will be codes in Python and instructions on how to run and adapt them. Topics that can be covered include (but are not limited to) - Background in linear algebra, vector calculus, probability, random variables/vectors and random processes - Background in convex optimisation, optimisation for machine learning, stochastic gradient descent (momentum, AdaGrad, Nesterov Accelerated Gradient, RMSProp and Adam) - Linear regression, regularisation (ridge, lasso, Elastic Net; training via proximal gradient methods and coordinate descent); maximum likelihood approaches; basis function methods; multi-output regression; bias-variance decomposition; methods to prevent overfitting; measures of error; cross validation, grid searches and nested cross validation; information criteria and Kullback-Liebler divergence for model selection; nonlinear least squares; Bayesian linear regression; maximum a-posterior estimates; connections to regularisation; evidence approximation and Laplace approximation - First look at non-parametric regression approaches; including local linear and polynomial regression and scatterplot smoothing; k-nearest neighbour regression; decision tree regression and cost complexity pruning; additive and generalised additive models - Linear classification:linear discriminant approaches; probabilistic generative models including naive Bayes classifier; maximum likelihood in classification models; Fisher's discriminant analysis; logistic regression; least-squares classification; error measures; generalised linear models; multi-class problems - Kernel methods and sparse kernel machines for regression: dual representations and kernel ridge regression; the Nadaraya-Watson model; support vector and relevance vector regression; Gaussian process models, including point estimates, mean-field variational inference and Markov Chain Monte Carlo sampling. Also covered are multi-output Gaussian process models, including coregionalisation models and low-rank approximations - An introduction to neural networks (multi-layer perceptrons MLPs) ; deep learning with MLPs, convolutional networks (CNNs); recurrent networks (RNNs); graph networks (GNNs); encoder-decoder models; auto-encoders; training stochastic gradient descent and backpropagation - Nonlinear classification: neural network classifiers, maximal margin classifiers and kernel support vector machines, relevance vector classification; decision tree classifiers; kNN classifiers; Gaussian process classification - Unsupervised learning: dimension reduction, clustering, density estimation: - principal component analysis, multi-dimensional scaling and independent component analysis; Isomap, diffusion maps, kernel PCA and local linear embedding; - clustering using k-means and k-mediod, hierarchical clustering, spectral clustering and Gaussian mixture models with expectation-maximization - density estimation using histograms, kernel density estimation, Gaussian mixture models, the Parzen window method and mixtures of experts - Advanced topics: ensemble methods: bagging (bootstrap aggregating) and boosting, including gradient boosting machines/random forests; - Time series and sequence data using embeddings, supervised machine learning, autoregression via linear/nonlinear state-space models and RNNs

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Rates

Rate

  • R1323

Package rates

  • 5h: R6613
  • 10h: R13226

free lesson

The free first lesson with Adrian allows you to get to know the tutor and discuss your needs and expectations.

  • 1h

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  • R1323/h

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Adrian's email address and phone number have been validated.

levels

Primary

Secondary

Matric/GCSE

AS Level

A Level

BTech

Adult education

Undergraduate

Masters

Diploma

Doctorate

MBA

Beginner

Intermediate

Advanced

Professional

Kids

Languages in which the lesson is available

English

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free lesson

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