Course ObjectiveThere are several learning objectives for this course. After completionof this course, the student should be able to:1. understand the capabilities and the limitations of machine learning, 2. implement machine learning algorithms in Python,3. know relevant machine learning algorithms for both supervised andunsupervised learning problems,4. select the right machine learning models for real-world use cases,5. understand when to apply online learning, reinforcement learning, and6. interpret the outcomes of machine learning algorithms. Course ContentMachine learning is the science of getting computers to act withoutbeing explicitly programmed. Machine learning is so pervasive today thatit is used in everyday life without knowing it. In this course, you willlearn about the most effective machine learning techniques, and gainpractice implementing them and getting them to work yourself. We willdiscuss the theoretical underpinnings as well as the practical know-howneeded to apply these techniques to new problems. Teaching MethodsLectures, guest speakers, and tutorials. Method of AssessmentTutorial assignments and a written exam. Entry RequirementsLinear Algebra (X_400042 ) and Statistics (X_400004) or equivalentLiteratureSlides and additional material that will be posted on Canvas. Target AudiencemBA, mBA-D, mMath, mSFM, mCS|Language of Tuition||English||Faculty||Faculty of Science||Course Coordinator||prof. dr. S. Bhulai||Examiner||prof. dr. S. Bhulai|prof. dr. S. BhulaiYou need to register for this course yourselfLast-minute registration is available for this course. This course is also available as: