| Course Code |
CSC368 |
| Course Title |
Machine Learning |
| Credit Hours |
3+0 |
| Prerequisites by Course(s) and Topics |
None |
| Assessment Instruments with Weights (homework, quizzes, midterms, final, programming assignments, lab work, etc.) |
SESSIONAL (Quizzes, Assignments, Presentations) =25 %
Midterm Exam =25 %
Final Exam = 50%
|
| Course Coordinator |
Muhammad Sohaib |
| URL (if any) |
|
| Current Catalog Description |
|
| Textbook (or Laboratory Manual for Laboratory Courses) |
Introduction to Machine Learning by Ethem Alpaydın by The MIT Press |
| Reference Material |
Bishop, Christopher, Neural Networks for Pattern Recognition, Oxford University Press, 1995 |
| Course Goals |
The aim of this course is to study, learn, and understand Machine learning / Deep learning. It enables computers to learn from examples and understand theoretical concepts of Machine learning techniques and use these techniques to perform different tasks including regression and classification. |
| Course Learning Outcomes (CLOs): |
| At the end of the course the students will be able to: | Domain | BT Level* |
|---|
| To understand the theory behind machine learning methods such as regression/classification. |
BT |
2 |
| To apply the concept of supervised and unsupervised learning in problem solving |
BT |
3 |
| To analyze the results generated from the implemented algorithms |
BT |
4 |
| To evaluate the performance of the implemented solution in the form of supervised and unsupervised learning techniques |
BT |
5 |
| * BT= Bloom’s Taxonomy, C=Cognitive domain, P=Psychomotor domain, A= Affective domain |
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| Topics Covered in the Course, with Number of Lectures on Each Topic (assume 15-week instruction and one-hour lectures) |
| Week | Lecture | Topics Covered |
|---|
| Week 1 |
1 |
Class introduction, Class overview: Class organization, topics overview, software etc. |
|
2 |
Introduction: what is ML; Problems, data, and tools; Python |
| Week 2 |
3 |
Artificial Intelligence and Machine Learning, Concept of Self-Learning |
|
4 |
Basics of Machine Learning, Different Types of Learning |
| Week 3 |
5 |
Data preprocessing, Feature extraction |
|
6 |
Data reduction, Dimensionality reduction |
| Week 4 |
7 |
Model selection, Model Generalization, and Overfitting |
|
8 |
Optimization of training Models |
| Week 5 |
9 |
Typical Tasks in Machine Learning |
|
10 |
Regression Vs Classification |
| Week 6 |
11 |
Linear Regression, Multivariate Linear Regression |
|
12 |
Non-Linear Regression |
| Week 7 |
13 |
Logistic Regression, Bayes’ Theorem |
|
14 |
Naïve Bayes Algorithm |
| Week 8 |
1 hours |
Mid Term |
| Week 9 |
15 |
Decision Trees |
|
16 |
Nearest Neighbor Algorithm |
| Week 10 |
17 |
Kernel Methods |
|
18 |
Support Vector Machines |
| Week 11 |
19 |
Markov Model |
|
20 |
Hidden Markov Model |
| Week 12 |
21 |
Introduction to Artificial Neural Network |
|
22 |
Perceptron |
| Week 13 |
23 |
Fully Connected Artificial Neural network |
|
24 |
Sparse Artificial Neural Network |
| Week 14 |
25 |
Ensemble Learning Part 1 |
|
26 |
Ensemble Learning Part 2 |
| Week 15 |
27 |
K-Mean Clustering |
|
28 |
Hierarchical Clustering |
| Week 16 |
29 |
Self-Organizing Maps Part I |
|
30 |
Self-Organizing Maps Part II |
| Week 17 |
2 hours |
Final Term |
|
| Laboratory Projects/Experiments Done in the Course |
|
| Programming Assignments Done in the Course |
Implementation of regression and classification models |
