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This Machine Learning course, offered by [YOUR COMPANY NAME], is designed to introduce students to the principles and practice of machine learning. It will cover a broad set of topics ranging from the basics of machine learning algorithms to the application and implementation of these techniques. Students will explore how these algorithms are used in data analysis, predictive modeling, and automation.
2. Instructor Information:
Instructor: [YOUR NAME]
Email: [YOUR EMAIL]
3. Learning Objectives:
Understand the principles of machine learning.
Learn various machine learning algorithms and models.
Know how machine learning techniques are applied in real-world scenarios.
Master the tools and techniques for handling, visualizing, analyzing, and interpreting data.
Develop practical skills required to implement machine learning solutions.
4. Course Schedule:
Week
Topic
Readings
1
Introduction to ML
"Machine Learning: A Probabilistic Perspective" by Kevin P. Murphy, Chapter 1
"Pattern Recognition and Machine Learning" by Christopher M. Bishop, Chapter 1
"Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron, Chapter 1
2
Supervised Learning
"Machine Learning Yearning" by Andrew Ng, Chapter 1
"Introduction to Statistical Learning" by Gareth James et al., Chapters 2 and 3
"Introduction to Statistical Learning" by Gareth James et al., Chapters 2 and 3
3
Unsupervised Learning
"Deep Learning" by Ian Goodfellow et al., Chapter 14
"Python Machine Learning" by Sebastian Raschka and Vahid Mirjalili, Chapter 9
Research papers: "Learning Representations by Back-Propagating Errors" by David E. Rumelhart et al., "Auto-Encoding Variational Bayes" by Diederik P. Kingma and Max Welling
5. Required Reading and Materials:
Murphy, K. (2012). Machine learning: A probabilistic perspective. MIT press.
Russell, S., & Norvig, P. (2016). Artificial intelligence: A modern approach. USA: Pearson.
Python for Data Analysis (2nd Edition) by Wes McKinney.
The website Kaggle for datasets and project ideas.
The Python programming language and Jupyter notebook software.
6. Assignments and Assessments:
Weekly quizzes to test understanding of lectures and readings.
Four assignment projects to give students practical experience implementing machine learning algorithms.
Mid-term and final exams testing comprehensive understanding of all material.
Final project requiring students to develop a machine learning solution for a real-world problem.
Participation in class discussions and online forums.
7. Required Resources:
Textbook: "Introduction to Machine Learning with Python" by Andreas C. Müller and Sarah Guido
Programming Environment: Python (Anaconda distribution recommended), Jupyter Notebooks
Additional Readings: Research papers, online tutorials, and documentation
8. Course Policy:
Attendance is crucial for understanding the material, it is strongly recommended.
All assignments must be submitted by the due date. Late submissions will result in a grade deduction.
Academic dishonesty will not be tolerated. Students are expected to understand and comply with all policies related to academic honesty.
All communications must be professional. This includes emails, discussion posts, and any other course-related communication.
Students are responsible for staying updated with the course schedule and any changes in course policies, assignments, or exams dates.
9. Grading Policy:
Grades will be determined based on quizzes, assignments, exams, the final project, and class participation. Detailed grading rubrics will be provided for each component.
10. Disclaimer:
This syllabus is not set in stone and it can be altered or modified as per the instructor's discretion at any given point in time, without any obligation of previously informing the students about the forthcoming changes. It is entirely the student's responsibility to regularly keep a check and stay knowledgeable about all the changes that have been implicated in the course until the present date.
