1st lesson belanja
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Adrian
- Rate RM510
- Response 7h
-
Students3
Number of students accompanied by Adrian since their arrival at Superprof
Number of students accompanied by Adrian since their arrival at Superprof
RM510/h
Contact
1st lesson belanja
- 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
- SPM
- +12
levels :
Primary
Secondary
SPM
Form 6
STPM
Adult education
Bachelor
Masters
Diploma
Doctorate
Beginner
Intermediate
Advanced
Professional
Kids
- English
All 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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Review
All of our reviews are collected by us and are 100% reliable. They correspond to real lessons experienced by the students of Adrian.
1 review
Perfect! Very knowledgeable tutor, friendly and engaging. Great learning experience
- Very able fast learner, pleasure to teachAdrian's response :
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Rates
Rate
- RM510
Pack rates
- 5h: RM2552
- 10h: RM5104
online
- RM510/h
free lessons
This first lesson is free to allow you to get to know your teacher so that they can best meet your needs.
- 1hr
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