Machine Learning Assignments

A collection of coursework assignments from a Master's-level Machine Learning course, typeset in LaTeX. Each assignment covers a specific topic and includes solved numericals, conceptual questions, and supplementary discussion.

Compiled PDFs are available in the ML-Wise2021_Nair/ folder.

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Contents

Assignment 1 — Foundations

#	Topic
A1.1	Machine Learning and its applications
A1.2	Least Mean Square (LMS) algorithm
A1.3	Confusion matrix and evaluation metrics
A1.4	Learning system for a Tic-Tac-Toe player

Assignment 2 — Loss Functions & Statistics

#	Topic
A2.1	Matching algorithms and loss functions to classification counterparts
A2.2	The Bias-Variance tradeoff
A2.3	Categorical and numerical features in a dataset
A2.4	Maximum Likelihood Estimation (MLE) for the Univariate Gaussian Distribution

Assignment 3 — Concept Learning

#	Topic
A3.1	Concept learning and related disciplines
A3.2	Use case of concept learning — Addison's disease
A3.3	Find-S algorithm and Candidate-Elimination algorithm
A3.4	Cross-validation as a classifier evaluation technique

Assignment 4 — Decision Trees

#	Topic
A4.1	Concept learning for Decision Trees
A4.2	Decision Tree fundamentals for Machine Learning
A4.3	Feature selection and challenges for Decision Trees (use case)
A4.4	Iterative Dichotomiser-3 (ID3) algorithm

Assignment 5 — Overfitting & Pruning

#	Topic
A5.1	Overfitting in Decision Trees with relation to Bias & Variance
A5.2	Tree pruning for Decision Trees (Reduced Error Pruning)
A5.3	Gain ratio as a split measure
A5.4	Regression Trees

Assignment 6 — Perceptrons & Neural Networks

#	Topic
A6.1	Perceptron for classification
A6.2	The Perceptron training rule (Delta rule)
A6.3	Neural Networks and their modalities
A6.4	Activation functions for Neural Networks (ReLU, Leaky ReLU variants)

Assignment 7 — Training & Backpropagation

#	Topic
A7.1	Gradient descent training rule
A7.2	Matching loss functions to activation functions
A7.3	The Backpropagation algorithm · Reference video
A7.4	Effect of learning rate as a hyperparameter

Assignment 8 — Sequential Models & Naive Bayes

#	Topic
A8.1	Non-sequential data classifiers, Feed-forward Neural Networks, BPTT, LSTM
A8.2	Naive Bayes and Maximum A-Posteriori Hypothesis (MAP)
A8.3	Naive Bayes (Numerical)
A8.4	Spam classification with SpamAssassin

Assignment 9 — Instance-Based Learning

#	Topic
A9.1	The k-Nearest Neighbor algorithm
A9.2	Regression & Classification algorithms
A9.3	k-NN (Numerical)
A9.4	Active Learning for Case-Based Reasoning

Assignment 10 — Clustering

#	Topic
A10.1	Supervised vs. Unsupervised learning
A10.2	k-Means algorithm in action
A10.3	Hierarchical Agglomerative Clustering
A10.4	Fuzzy C-Means algorithm

Assignment 11 — LVQ & Reinforcement Learning

#	Topic
A11.1	Learning Vector Quantization (LVQ) algorithm
A11.2	Reinforcement Learning and its components
A11.3	The Value-Iteration algorithm
A11.4	The Value-Iteration algorithm (Episodic process)

Assignment 12 — Association Rules

#	Topic
A12.1	Association rules
A12.2	Frequent Itemset Mining (Exercise)
A12.3	Support and Confidence measures for association rules (Numerical)
A12.4	Apriori vs. ECLAT

Related

For hands-on practical implementations of the topics covered here, see the companion repository:

ranjiGT/ai-machine-learning-codes — practical ML code exercises and implementations.

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Prerequisites

To compile the .tex sources you will need:

• A LaTeX distribution such as TeX Live or MiKTeX
• A PDF viewer for reading the compiled output

Pre-compiled PDFs are already available in ML-Wise2021_Nair/ if you do not want to build from source.

License

See LICENSE for details.