Machine Learning

Learning Outcomes:

• Understanding fundamental machine learning concepts and algorithms
• Applying supervised and unsupervised learning techniques to real-world problems
• Implementing decision trees, linear regression, and logistic regression models
• Utilizing probabilistic methods like Naive Bayes and Hidden Markov Models
• Employing support vector machines and kernel methods for classification tasks
• Designing and training artificial neural networks
• Exploring evolutionary algorithms and genetic programming
• Developing reinforcement learning agents using various techniques
• Evaluating machine learning model performance using appropriate metrics
• Analyzing the ethical implications of machine learning applications
• Implementing learning from demonstration techniques
• Addressing challenges in machine learning such as overfitting and bias

Skills for module:

Python

R

Matlab

Machine Learning

Deep Learning

Artificial Intelligence

Reinforcement Learning

Intelligent Agents

Data Science

Hyperparameters

Boosting

Data Visualisation

Neural Networks

Scikit Learn

Pandas

NumPy

Matplotlib

TensorFlow

PyTorch

Jupyter Notebooks

Probability

Statistics

Linear Algebra

Algorithms

Data Structures

Problem Solving

Critical Thinking

Time Management

Continuous Integration

Mathematics