AI Algorithms I

AIDI-1002 — Mastery Overview

6:10 PM - 8:00 PM
1
Course Introduction and Machine Learning

Review course expectations and key concepts of ML.

Learning Outcomes:
Welcome and Intro; Supervised vs Unsupervised; Regression vs Classification; Data Pre-processing impact.
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Python Code Reference Required Readings
2
Linear Regression and Gradient Descent

Linear Regression: Hypothesis, loss, and cost functions. Gradient Descent: Optimization, learning rates, and best-fit determination.

Learning Outcomes:
• Explain Gradient Descent algorithm & Learning Rate significance. • Discuss Overfitting and methods to address it. • Python Code: Week2a-SLR, Week2b-MLR. • Datasets: Carslm, Mtcars, InsRaw.
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Python Code Reference Required Readings
3
Logistical Regression and Algorithm Evaluation

Logistic Regression: Decision boundaries and multiclass classification. Evaluation: Bias-variance trade-off and evaluation matrices.

Learning Outcomes:
• Apply regularization to avoid overfitting. • Split datasets into training, cross-validation, and test sets. • Python Code: Week3a-LogReg-lbfgs, Week3b-LogMulti. • Datasets: Iris, Diabetes.
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Python Code Reference Required Readings
4
Machine Learning Model Implementation

End-to-end implementation focusing on data pre-processing, visualization, and prediction.

Learning Outcomes:
• Perform training/test split of data. • Identify strengths and weaknesses of linear models. • Python Code: Week4a-Scalers. • ASSESSMENT: Assignment #1 Due.
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Python Code Reference Required Readings Graded Item Due
5
SVM and kNN Modelling Techniques

Large margin classification with SVM and instance-based learning with kNN.

Learning Outcomes:
• Discuss Kernel SVM for non-linear problems. • Analyze hyperparameter k for kNN. • Python Code: Week5a-SVM-Class, Week5a-kNNClass. • ASSESSMENT: Assignment #2 Due.
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Python Code Reference Required Readings Graded Item Due
6
Naive Bayes

Review of Bayes Theorem and probabilistic classification.

Learning Outcomes:
• Explain Bayes Theorem. • Analyze Naive Bayes as a probabilistic classifier. • Python Code: Week6a-NBClass. • ASSESSMENT: Mid-term Test.
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Python Code Reference Required Readings Graded Item Due
7
Tree-Based and Ensemble Models

Decision Trees, Random Forests, and Ensemble techniques like Bagging/Boosting.

Learning Outcomes:
• Explain Pruning, Random Forest, and Ensemble Methods. • Discuss Adaboost and Gradient Boosting. • Python Code: Week7a-DtRandomF, Week7b-ExtraTree, Week7d-Tutorial-Pruning. • ASSESSMENT: Assignment #2 Due.
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Python Code Reference Required Readings Graded Item Due
8
Classification Modelling

Building and evaluating classification models using supervised learning.

Learning Outcomes:
• Perform EDA, Pre-processing, and Visualization. • Fit model and select evaluation matrices. • Python Code: Week8a-Class Modelling.
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Python Code Reference Required Readings
9
Neural Networks

Introduction to NN architecture, forward, and backward propagation.

Learning Outcomes:
• Examine NN architecture and Deep Learning. • Analyze backward propagation and automatic feature extraction. • Python Code: Week9a-NN, Week9b-NN. • ASSESSMENT: Assignment #3 Due.
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Python Code Reference Required Readings Graded Item Due
10
Neural Network Application and PCA

Applying NN to classification and dimensionality reduction via PCA.

Learning Outcomes:
• Compare NN with Logistic Regression. • Discuss Dimensionality Reduction with PCA. • Python Code: Week10a-PCA, Week10b-Tutorial-PCA. • Dataset: Cancer Dataset.
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Python Code Reference Required Readings
Item Weight Raw Score Performance Status
Assignment #1 - Multiple Regression 7.50% 6 / 8
Graded
Assignment #2 - Logistical Regression 7.50% - Waiting for results... Pending
Assignment #3 - SVM 7.50% - Waiting for results... Pending
Assignment #4 - Decision Trees 7.50% - Waiting for results... Pending
Mid-Term Test 20.00% - Waiting for results... Pending
Final Project 20.00% - Waiting for results... Pending
Mid-term Test 20.00% - Waiting for results... Pending
Assignment #3 - SVM 7.50% - Waiting for results... Pending