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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
- Euclidean distance
- Cityblock distance
- Cosine distance
- Correlation distance
- Mahalanobis distance
- Hamming distance
- MAD (Mean Absolute Deviation)
- RMS (Root Mean Square)
- Mean squared deviation
Dimensionality Reduction
-
Principal Component Analysis (PCA)
- Linear PCA
- Kernel PCA
- Probabilistic PCA
- Independent Component Analysis (ICA)
- Multidimensional scaling
Regularized Regression Methods
- Basic concepts 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, 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
- Multilayer perceptrons: forward pass and backpropagation
- Regularization
- Recurrent neural networks (RNN)
- Convolutional neural networks (Convnets)
- Autoencoders
- Hyperparameters
Requirements
This course is intended for individuals who already possess a background in data science and statistics.
21 Hours
Testimonials (1)
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
