| Course Code |
CSC |
| Course Title |
Advanced Machine Learning |
| Credit Hours |
3 |
| Prerequisites by Course(s) and Topics |
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| Assessment Instruments with Weights (homework, quizzes, midterms, final, programming assignments, lab work, etc.) |
SESSIONAL (Quizzes, Assignments, Presentations) =25 %
Midterm Exam =25 %
Final Exam = 50%
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| Course Coordinator |
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| URL (if any) |
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| Current Catalog Description |
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| Textbook (or Laboratory Manual for Laboratory Courses) |
Marco Gori , Machine Learning: A Constraint-Based Approach, Morgan Kaufmann. 2017 Ethem Alpaydin, Machine Learning: The New AI, MIT Press-2016 |
| Reference Material |
Ryszard, S., Michalski, J. G. Carbonell and Tom M. Mitchell, Machine Learning: An Artificial Intelligence Approach, Volume 1, Elsevier. 2014 , Stephen Marsland, Taylor & Francis 2009. Machine Learning: An Algorithmic Perspectiv |
| Course Goals |
After completing this course, the student will be able to Appreciate the importance of visualization in the data analytics solution Apply structured thinking to unstructured problems Understand a very broad collection of machine learning algorithms and problems Learn algorithmic topics of machine learning and mathematically deep enough to introduce the required theory Develop an appreciation for what is involved in learning from data. |
| Course Learning Outcomes (CLOs): |
| At the end of the course the students will be able to: | Domain | BT Level* |
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| To understand the basic theory underlying machine learning |
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| be able to formulate machine learning problems corresponding to different applications. |
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| understand a range of machine learning algorithms along with their strengths and weaknesses. |
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| To be able to apply machine learning algorithms to solve problems of moderate complexity. |
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| To apply the algorithms to a real-world problem, optimize the models learned and report on the expected accuracy that can be achieved by applying the models. |
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| Learn algorithmic topics of machine learning and mathematically deep enough to introduce the required theory |
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| * 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 |
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| Week 1 |
1 |
introduction to machine learning and its applications |
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2 |
supervise, unsupervised and reinforcement learning |
| Week 2 |
3 |
Linear Regression with One Variable |
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4 |
Linear Regression with Multiple Variables |
| Week 3 |
5 |
Logistic Regression and its application |
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6 |
Regularization and its description |
| Week 4 |
7 |
Neural Networks: Representation |
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8 |
ANN and its applications |
| Week 5 |
9 |
Perceptron's ,Multilayer Networks and Back Propagation Algorithms |
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10 |
Advanced Topics , Genetic Algorithms, Hypothesis Space Search,Genetic Programming , Models of Evolution and Learning |
| Week 6 |
11 |
Advice for Applying Machine Learning |
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12 |
Machine Learning System Design |
| Week 7 |
13 |
Support Vector Machines |
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14 |
Support Vector Machines and its variants |
| Week 8 |
1 hours |
Mid Term |
| Week 9 |
15 |
Support Vector Machines and its application |
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16 |
Random Forest classifier |
| Week 10 |
17 |
Naïve Bayes Classifier |
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18 |
BayesianBelief Network – EM Algorithm – Probability Learning – Sample Complexity |
| Week 11 |
19 |
K- Nearest Neighbour Learning |
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20 |
Locally weighted Regression – Radial Bases Functions – Case Based Learning. |
| Week 12 |
21 |
Unsupervised Learning |
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22 |
Dimensionality Reduction |
| Week 13 |
23 |
Anomaly Detection |
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24 |
Recommender Systems |
| Week 14 |
25 |
Large Scale Machine Learning |
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26 |
Application of AML |
| Week 15 |
27 |
earning Sets of Rules – Sequential Covering Algorithm – Learning Rule Set – First Order Rules – Sets of First Order Rules |
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28 |
Induction on Inverted Deduction – Inverting Resolution – Analytical Learning |
| Week 16 |
29 |
Explanation Base Learning – FOCL Algorithm |
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30 |
Reinforcement Learning – Task – Q-Learning – Temporal Difference Learning. “Current Streams of Thought |
| Week 17 |
2 hours |
Final Term |
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| Laboratory Projects/Experiments Done in the Course |
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| Programming Assignments Done in the Course |
Research paper regrading the machine learning algorithm |
