Python Programming for Machine Learning Applications (BECL657D)

Python Programming for Machine Learning Applications

Course Code BECL657D
CIE Marks 50
Teaching Hours/Week (L:T:P: S) 0:0:2:0 
SEE Marks 50
Credits 01 
Exam Hours 3
Examination type (SEE) Practical

 Experiments

 1 Solve the Tic-Tac-Toe problem using the Depth First Search technique.

 2 Show that the 8-puzzle states are divided into two disjoint sets, such that any state is reachable from any other state in the same set, while no state is reachable from any state in the other set.

3 To represent and evaluate different scenarios using predicate logic and knowledge rules.

4 To apply the Find-S and Candidate Elimination algorithms to a concept learning task and compare their inductive biases and outputs.

5 To construct a decision tree using the ID3 algorithm on a simple classification dataset

6 To assess how the ID3 algorithm performs on datasets with varying characteristics and complexity, examining overfitting, underfitting, and decision tree depth.

7 To examine different types of machine learning approaches (Supervised, Unsupervised, Semi-supervised, and Reinforcement Learning) by setting up a basic classification problem and exploring how each type applies differently

8 To understand how Find-S and Candidate Elimination algorithms search through the hypothesis space in concept learning tasks, and to observe the role of inductive bias in shaping the learned concept.

9 To go through all stages of a real-life machine learning project, from data collection to model fine-tuning, using a regression dataset like the "California Housing Prices."

10 To perform binary and multiclass classification on the MNIST dataset, analyze performance metrics, and perform error analysis

Suggested Learning Resources:

Text Book:

1. Stuart J. Russell and Peter Norvig , Artificial Intelligence, 3rd Edition, Pearson,2015

2. Elaine Rich, Kevin Knight, Artificial Intelligence, 3rd Edition,Tata McGraw Hill,2013.

3. Tom M. Mitchell, Machine Learning, McGraw-Hill Education, 2013

4. AurelienGeron, Hands-on Machine Learning with Scikit-Learn &Tensor Flow , O’Reilly, Shroff

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