Welcome to ECE 364! In this course we present basic concepts in machine learning for predictive data analytics:
- Information-based learning: decision trees; Shannon’s entropy model; information gain; ID3 algorithm; feature selection; predicting continuous targets; tree pruning; model ensembles: boosting, bagging.
- Similarity-based learning: feature spac, distance metrics, nearest neighbor algorithm, noisy data, predicting continuous targets, various measures of similarity, feature selection.
- Probability-based learning: Bayes' theorem; Bayesian prediction; curse of dimensionality; conditional independence and factorization; Naive Bayes model; smoothing; continuous features: probability density functions, binning; Bayesian networks.
- Error-based learning: simple linear regression; error surface; multivariate linear regression; gradient descent; learning rate; handling categorical features; modeling nonlinear relationships; multinomial logistic regression; support vector machines.
- Deep learning: artificial neural networks; activation functions; backpropagation and gradient descent; vanishing gradients; weight initialization; categorical target features: softmax layer and cross-entropy loss; dropout.
- Evaluation: misclassification rate; confusion matrix: precision, recall, F1 measure, profit/loss; prediction scores: receiver operating characteristic curve, Kolmogorov-Smirnov statistic, gain/lift; measures of error; evaluating models after deployment.
- The art of machine learning: perspectives on prediction models: parametric vs. nonparametric, generative vs. discriminative; choosing a machine learning approach: no free lunch theorem, project requirements, data considerations.
- Canvas web site
- Zoom links and TA contact information can be found on Canvas.
- Instructor: Niraj K. Jha
- TAs: Sayeri Lala, Margarita Belova
- Lectures: M/W 3:00-4:20pm (EQuad B205)
- Office hours:
- Niraj K. Jha: M/W 2-3pm (EQuad B205)
- Sayeri Lala: Tu: 4-5pm, Th: 11-12pm (EQuad B321)
- Margarita Belova: M: 11-12pm, W:1-2pm (EQuad B321)
- Assignments (25%):
- Mid-term exam (25%)
- Final exam (50%)
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