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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 metrics
- Euclidean
- Cityblock
- Cosine
- Correlation
- Mahalanobis
- Hamming
- MAD
- RMS
- Mean squared deviation
Dimensionality Reduction
-
Principal Component Analysis (PCA)
- Linear PCA
- Kernel PCA
- Probabilistic PCA
- Independent Component Analysis (ICA)
- Multidimensional scaling
Alternative Regression Methods
- Basics of regularization
- Ridge regression
- Lasso regression
- Principal component regression (PCR)
Clustering
- K-means
- K-medoids
- DBSCAN
- Hierarchical clustering
- Markov Cluster Algorithm
- Fuzzy C-means clustering
Standard Machine Learning Models
(NearestNeighbors, DecisionTree, LightGBM, XGBoost, EvoTrees, and LIBSVM packages)
- Gradient boosting concepts
- K-Nearest Neighbors (KNN)
- Decision tree models
- Random forest models
- XGBoost
- EvoTrees
- Support Vector Machines (SVM)
Artificial Neural Networks
(Flux package)
- Stochastic gradient descent and strategies
- Forward propagation and backpropagation in Multilayer Perceptrons
- Regularization
- Recurrent Neural Networks (RNN)
- Convolutional Neural Networks (ConvNets)
- Autoencoders
- Hyperparameters
Requirements
This course is intended for participants who already have a background in data science and statistics.
21 Hours
Testimonials (2)
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
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