Course Goals:
- Understand the fundamental concepts of machine learning.
- Develop practical skills in building and applying machine learning models.
- Explore the applications of machine learning in various domains.
Prerequisites:
- Basic understanding of programming (Python is recommended)
- Familiarity with linear algebra and calculus
Module 1: Introduction to Machine Learning
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- What is machine learning?
- Types of machine learning (supervised, unsupervised, reinforcement)
- Key concepts: data, algorithms, models, training, testing
- Applications of machine learning
Module 2: Data Preparation and Exploration
- Data collection and cleaning
- Data preprocessing (normalization, standardization, handling missing values)
- Exploratory data analysis (EDA) using visualization techniques
Module 3: Supervised Learning
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- Linear regression
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- Logistic regression
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- Decision trees and random forests
- Support vector machines
(SVMs) - Neural networks
Module 4: Unsupervised Learning
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- Clustering algorithms (k-means, hierarchical clustering)
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- Dimensionality reduction (PCA, t-SNE)
- Anomaly detection
Module 5: Reinforcement Learning
- Markov decision processes (MDPs)
- Q-learning
- Deep Q-networks (DQN)
Module 6: Model Evaluation and Tuning
- Evaluation metrics (accuracy, precision, recall, F1-score)
- Model selection and hyperparameter tuning
- Cross-validation
Module 7: Machine Learning Applications
- Natural language processing (NLP)
- Computer vision
- Recommendation systems
- Fraud detection
- Medical diagnosis
Project:
- Students will work on a group project to apply machine learning techniques to solve a real-world problem.
Assessment:
- Assignments
- Quizzes
- Midterm exam
- Final project
- Class participation
