Fundamentals of Machine Learning
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Fundamentals of Machine Learning Syllabus — Unit-wise Content
The official GTU curriculum for Fundamentals of Machine Learning (subject code 4341603) as prescribed for Information Technology · Semester 4 divides the course into 6 units, covering 36 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (4341603)Unit 1 Introduction to machine learning
- 1.1. Describe basic concept of machine learning and its applications
- 1.1.1. Overview of Human Learning and Machine Learning
- 1.1.2. Types of Machine Learning
- 1.1.3. Applications of Machine Learning
- 1.1.4. Tools and Technology for Machine Learning
Unit 2 Preparing to Model
- 2.1. Describe different types of Machine learning Activities.
- 2.2. Explain types of data and data preprocessing.
- 2.1.1. Machine Learning activities
- 2.1.2. Types of data in Machine Learning
- 2.1.3. Structures of data
- 2.1.4. Data quality and remediation
- 2.1.5. Data Pre-Processing Dimensionality reduction Feature subset selection
Unit 3 Modeling and Evaluation
- 3.1. selecting a machine learning model
- 3.2. Train the model for supervised learning
- 3.3. Evaluate the prepared model.
- 3.1.1. Selecting a Model Predictive/Descriptive
- 3.2.1. Training a Model for supervised learning: ● Holdout method ● K-fold Cross-validation method
- 3.3.1. Model representation and interpretability
- 3.3.2. Evaluating performance of a model Confusion Matrix
- 3.3.3. Improving Performance of a model
Unit 4 Supervised Learning - Classification
- 4.1. Describe supervised learning
- 4.2. Explain classification Algorithms.
- 4.3. Explain Regression.
- 4.1.1. Introduction to supervised learning.
- 4.1.2. Classification Model
- 4.1.3. Learning steps Fundamentals of Machine Learning Course Code: 4341603 and Regression
- 4.2.1. Classification Algorithms k-Nearest Neighbor (kNN) Support Vector Machines 4.3.1Regression Simple linear regression Multiple linear regression Logistic regression
Unit 5 Unsupervised Learning
- 5.1. Explain unsupervised learning
- 5.2. Describe Clustering
- 5.3. Describe pattern finding using association rule
- 5.1.1. Supervised vs. Unsupervised Learning
- 5.1.2. Applications of unsupervised learning
- 5.2.1. Clustering K-means Clustering algorithm
- 5.3.1. Finding pattern using Association Rule Apriori algorithm
Unit 6 Python libraries for Machine learning
- 6.1. Explain the use of existing machine learning python libraries.
- 6.1.4. Scikit-learn Note: The UOs need to be formulated at the ‘Application Level’ and above of Revised Bloom’s Taxonomy’ to accelerate the attainment of the COs and the competency.
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