Lectures

Lecture 1

Introduction to Machine Learning

Basic Concepts

Learning Paradigms

Lecture 2

Basic Concepts

Naive Bayes

Performance Metrics

Lecture 3

Nearest Neighbors

Local Learning

Curse of Dimensionality

Lecture 4

Decision Trees

Random Forests

Lecture 5

Kernel Methods

Ridge Regression

Lecture 6

Support Vector Machines

Logistic Regression

Lecture 7

Loss Functions and Optimization

Gradient Descent

Code

Lecture 8

Neural Networks

Introduction to Deep Learning

Dropout

Code

Lecture 9

Convolutional Neural Networks

Convolutional Layer

Pooling Layer

Lecture 10

Bag-of-Words

Term Frequency - Inverse Document Frequency

Bag-of-Visual-Words

Histogram of Oriented Gradients

Lecture 11

K-means

Clustering Goodness

Soft k-means

Kernel k-means

Lecture 12

DBSCAN

Clustering by unmasking

Hierarchical Clustering

Lecture 13

Principal Component Analysis

t-SNE

Labs

Installing Anaconda - Windows

Installing Anaconda - Linux

Lab 1

Introduction to Python

Introduction to Numpy

Introduction to Matplotlib

Solution

Lab 2

K-Nearest Neighbors

Naive Bayes

Solution

Lab 3

Kernel Ridge Regression

Support Vector Machines

Solution

Lab 4

Neural Networks

Convolutional Neural Networks

Solution

Lab 5

K-means

DBSCAN

Solution

Lab 6

K-means

DBSCAN

Hierarchical Clustering