# Machine Learning

**Course Objectives: ** This course on Machine Learning will explain how to build systems that learn and adapt using real-world applications (such as robotics and brain wave signal understanding). Some of the topics to be covered include reinforcement learning, neural networks, genetic algorithms and genetic programming, parametric learning (density estimation), clustering, and so forth. The course will be project-oriented, with emphasis placed on writing software implementations of learning algorithms applied to real-world problems.

**Course Outline:**

- Introduction to Machine Learning
- Linear Regression with one Variable
- Multivariate Linear Regression.
- Regularizes, Learning curves
- Optimization
- Classification: Linear classification
- Support Vector Machines
- Kernels
- Neural Network: Representation
- Neural Network: Learning, Back-propagation
- Convolutional Neural Network
- Unsupervised Learning: Clustering
- Anomaly Detection
- Project Presentations

**Practice with PYTHON:**

- Basics of PYTHON
- Reading and manipulate a data by Pandas
- Arrays and Normal Equation
- Linear/Logistic regression
- Preprocessing the data for ML Algorithms
- Classification - MINST dataset
- NLP simplified

**TextBook:** T. Mitchell, Machine Learning, McGraw-Hill

**Laboratory**: PYTHON

**Course Evaluation Method**

Project 40%

Quiz 2 x 5%

Lab Assignment 5 x 2%

Final Exam 40%