Course Description:
This course provides an introduction to the most important techniques used in the development of machine learning models. The course is structured in a way to provide hands-on experience and to help understand the mathematical, statistical, and computational logic behind the learning algorithms.
Learning Objectives:
- Develop an understanding of the theory behind machine learning algorithms.
- Gain practical experience in implementing machine learning algorithms and apply them to solve real-world problems.
- Develop proficiency in interpreting the results of machine learning models.
Grading Policy
- Project: 30%
- Midterm Exam: 30%
- Final Exam: 40%
Week 1: Introduction to Machine Learning
- Welcome
- Linear Regression with One Variable (Lab Document, csv file)
- Linear Algebra Review
Week 2: Linear Regression with Multiple Variables
- Linear Regression with Multiple Variables (Lab Document)
Week 3: Logistic Regression and Regularization
- Logistic Regression
- Regularization
Week 4: Neural Networks: Representation (Lab Document)
- Neural Networks: Representation
Week 5: Neural Networks: Learning
- Neural Networks: Learning
Week 6: Application Advice and System Design in Machine Learning
- Advice for Applying Machine Learning
- Machine Learning System Design
Week 7: Midterm Exam
Week 8: Support Vector Machines (Lab Document)
- Support Vector Machines
Week 9: Clustering and Dimensionality Reduction (Lab Document)
- Clustering
- Dimensionality Reduction
Week 10: Anomaly Detection and Recommender Systems (Lab Document)
- Anomaly Detection
- Recommender Systems
Week 11-14: Term Project Debugging and Presentations
For this Machine Learning course, we will be diligently following the syllabus outlined by Professor Andrew Ng for Stanford University’s CS229 course. This will ensure that the content delivered is of high quality and adheres to a standard that is recognized globally, providing students with a comprehensive understanding of machine learning concepts, theories, and applications.