Course Outline
Machine Learning Algorithms in Julia
Foundational concepts
- Supervised and unsupervised learning
- Cross-validation and model selection
- Bias/variance tradeoff
Linear and logistic regression
(NaiveBayes and GLM)
- Foundational concepts
- Fitting linear regression models
- Model diagnostics
- Naive Bayes
- Fitting a logistic regression model
- Model diagnostics
- Model selection methods
Distance metrics
- Understanding distance concepts
- Euclidean distance
- Cityblock distance
- Cosine distance
- Correlation distance
- Mahalanobis distance
- Hamming distance
- MAD
- RMS
- Mean squared deviation
Dimensionality reduction
-
Principal Component Analysis (PCA)
- Linear PCA
- Kernel PCA
- Probabilistic PCA
- Independent Component Analysis (ICA)
- Multidimensional scaling
Regularised regression methods
- Basic concepts of regularization
- Ridge regression
- Lasso regression
- Principal component regression (PCR)
Clustering techniques
- K-means
- K-medoids
- DBSCAN
- Hierarchical clustering
- Markov Cluster Algorithm
- Fuzzy C-means clustering
Standard machine learning models
(NearestNeighbors, DecisionTree, LightGBM, XGBoost, EvoTrees, LIBSVM packages)
- Gradient boosting concepts
- K-nearest neighbours (KNN)
- Decision tree models
- Random forest models
- XGBoost
- EvoTrees
- Support vector machines (SVM)
Artificial neural networks
(Flux package)
- Stochastic gradient descent and strategies
- Multilayer perceptrons: forward feed and back propagation
- Regularization
- Recurrent neural networks (RNN)
- Convolutional neural networks (ConvNets)
- Autoencoders
- Hyperparameters
Requirements
This course is intended for participants who already possess a background in data science and statistics.
Testimonials (3)
I really liked the end where we took the time to play around with CHAT GPT. The room was not set up the best for this- instead of one large table a couple of small ones so we could get into small groups and brainstorm would have helped
Nola - Laramie County Community College
Course - Artificial Intelligence (AI) Overview
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking
That it was applying real company data. Trainer had a very good approach by making trainees participate and compete
