Intelligent assistants like Alexa and Siri, image searches that found the topic of a photo, and self-driving cars – these intelligent systems used machine learning to develop their expertise. In this module, I learned about various machine learning techniques but concentrated on deep neural learning. I learned about the underlying theory and got hands-on experience creating, training, evaluating, and using machine learning systems. I also examined how these technologies were used and misused and what that meant for our societies and communities.

 

Learning Outcomes

  • Learned analytical solutions for first and second-order ordinary differential equations, and mastered linear algebra.
  • Applied calculus of functions of several variables, including vector calculus and partial differential equations.
  • Contextualized mathematical methods within real-world applications, focusing on analytical skills.
  • Prepared for further studies in applied sciences with a foundational understanding of essential mathematical methods.

Feedback on my assingments

The feedback for both assignments in the mathematical methods module reflects a strong performance with commendations for clear solution presentation and mathematical communication. Key areas for improvement include mastering implicit differentiation and solving first-order differential equations, with a general recommendation to practice verifying solutions as a check against errors.

Reflections

The feedback highlights a solid foundation in mathematical understanding and communication, with specific technical areas needing further attention. The process of checking work, as emphasized in the feedback, is a crucial skill that enhances accuracy and confidence in one's solutions.

Professional Skills Matrix learnt and Action Plan

 

Skills Gained or Enhanced:

  • Technical Proficiency Demonstrated capability in various mathematical techniques, with room to deepen understanding in differential equations and implicit differentiation.
  • Analytical Thinking: Ability to approach problems methodically and communicate solutions clearly.
  • Attention to Detail: Recognition of the importance of precision in mathematical notation and the process of checking work to ensure accuracy.

Action Plan:

  • Targeted Revision: Focus on revising implicit differentiation and first-order differential equations. Utilize course materials and seek additional resources such as online tutorials or textbooks that offer exercises with solutions for practice.
  • Practice with Purpose: Engage in regular practice sessions, specifically targeting the areas of improvement identified. Implement a habit of checking solutions rigorously by reverse engineering problems (e.g., differentiating to check integration).
  • Seek Feedback: Continue to seek and reflect on feedback, identifying patterns in mistakes or misunderstandings. Consider forming study groups or seeking out a mentor for discussions on challenging topics.
  • Enhance Notation Precision: Pay extra attention to mathematical notation, understanding its importance in conveying solutions accurately. Practice by explaining your reasoning and steps to others, which can help clarify your own understanding.
  • Apply Knowledge: Look for opportunities to apply mathematical concepts in real-world contexts or in other areas of study, reinforcing understanding and discovering the practical implications of mathematical methods.