Introduction to Machine Learning

Course details

This course provides an introduction to the core concepts and techniques of machine learning, setting the foundation for deeper dives into specialized ML topics. It is intended for students to develop an understanding of algorithms, supervised and unsupervised learning, and how ML can be applied to real-world problems.

Start Date

TBD

Duration

14-15 weeks

Prerequisites

Programming in Python and R, Statistical Analysis - I.

Learning Objectives

Understand the foundational principles and techniques of machine learning.
Familiarize with key ML algorithms and their applications.
Implement machine learning algorithms using Python and R.
Analyze the strengths and weaknesses of different algorithms.
Solve real-world problems using appropriate machine learning models.

Key Features

Weekly interactive lectures detailing ML concepts.
Hands-on labs with real-world datasets.
Access to cloud platforms for computational tasks.
Collaboration on group projects to solve industry problems.
Continuous evaluation through quizzes, assignments, and exams.

Learning path

Unit 1: Foundations of Machine Learning (3)
Unit 2: Supervised Learning (9)
Unit 3: Unsupervised Learning (6)
Unit 4: Ensemble and Advanced Methods (5)
Unit 5: Practical Machine Learning (3)
Unit 6: Ethical Considerations in ML (2)

Topics covered

Introduction to Machine Learning and its significance
Overview of ML Types: Supervised, Unsupervised, Reinforcement
Machine Learning vs. Traditional Programming

Introduction to Supervised Learning
Linear Regression and its variants
Logistic Regression
K-Nearest Neighbors (K-NN)
Decision Trees and Random Forests
Support Vector Machines (SVM)
Naive Bayes Classifier
Neural Networks Basics
Evaluation Metrics for Classification and Regression

Introduction to Unsupervised Learning
Clustering Methods: K-Means, Hierarchical
Principal Component Analysis (PCA)
Association Rule Mining
Anomaly Detection
Evaluating Clustering Performance

Introduction to Ensemble Learning
Bagging and Boosting
Gradient Boosted Trees: XGBoost, LightGBM
Introduction to Deep Learning
Convolutional Neural Networks (Basic)

Feature Engineering and Selection
Overfitting, Underfitting, and Model Selection
Tools and Libraries: Scikit-learn, TensorFlow, Keras

Bias and Fairness in Machine Learning Models
Transparency and Interpretability in ML

Grading:

Class Participation: 10%
Weekly Assignments: 30%
Group Project: 20%
Mid-Term Examination: 20%
Final Examination: 20%

Reading Material: Essential textbooks detailing ML algorithms, combined with research articles, case studies, and online tutorials. All resources will be made available on the LMS.

Assignments: Weekly tasks focusing on the implementation and evaluation of ML algorithms on diverse datasets.

Faculty: A distinguished academic with expertise in machine learning and its applications.

Teaching Instructors: TBD